library(tidyverse)
library(FIELDimageR)
library(raster)
library(readxl)
library(gsheet)
library(foreach)
library(agricolae)
library(reshape2)
library(cowplot)
library(gbm)
library(lime)
library(lme4)
library(DescTools)
library(factoextra)
library(FactoMineR)
library(corrplot)
library(magick)
library(patchwork)
library(forcats)
library(ggdist)
Load pictures names
pics<-list.files("./pics/01-Calonectria_leaf_bligth")
length(pics)
## [1] 300
# write(pics, "pics_names.txt")
List of RGB-based spectral indices to be calculated in the image
# Vegetation indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
Load a single image
#Choose one image to prepare the pipeline
EX.L1<-stack(paste("./pics/01-Calonectria_leaf_bligth/",pics[254],sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot =T)
## [1] "3 layers available"
# plotRGB(EX.L.Shape)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Blue"), plot = T)
## [1] "3 layers available"
plot(EX1.Indices$Blue)
hist(EX1.Indices$Blue)
# EX.L2<-fieldMask(mosaic=EX.L1, myIndex = "Red", cropValue=200, cropAbove=T, plot = T)
# EX.L2<-fieldMask(mosaic=EX.L1, index = "BI", cropValue=1, cropAbove=F, plot = T)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=175, cropAbove=T, plot = T)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
# plotRGB(EX.L2$newMosaic)
plotRGB(EX.L2$newMosaic)
class(EX.L2$mask)
## [1] "RasterStack"
## attr(,"package")
## [1] "raster"
rgb_fig = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_void()+
coord_fixed()
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
cut = mask(EX.L1, EX.L2$newMosaic)
plot(cut)
EX.L4<-fieldIndex(mosaic=cut,
index = index)
## [1] "3 layers available"
plot(EX.L4$BGI)
gli_fig = as.data.frame(EX.L4$BGI, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = BGI))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_void()+
coord_fixed()
plot_grid(rgb_fig, gli_fig, axis = "b",
rel_widths = c(0.9,1),
labels = c("RGB", "BGI"),
scale = 0.90)
# ggsave("figs/leaf_gli.png",dpi = 600, height = 3, width =10 )
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
gather(1:(3+length(index)), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[30])
## `summarise()` ungrouping output (override with `.groups` argument)
pics<-list.files("./pics/01-Wheat_leaf_blast")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-Wheat_leaf_blast/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=10)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=180, cropAbove=T, plot = F)
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_WLB.txt")
box = read.table("data/indexes_WLB.txt")
# sev_data = gsheet2tbl("https://docs.google.com/spreadsheets/d/106sg_O8DeALZpWnxERQNgk8UKgsFbhobWwrl4JXOzbo/edit#gid=0")
sev = gsheet2tbl("https://docs.google.com/spreadsheets/d/106sg_O8DeALZpWnxERQNgk8UKgsFbhobWwrl4JXOzbo/edit#gid=0")
sev
## # A tibble: 200 x 5
## inoculated code rep pic_name sev
## <chr> <chr> <dbl> <chr> <dbl>
## 1 BRS Guamirim 42 1 G_42_R1 0.18
## 2 BRS Guamirim 42 2 G_42_R2 0.38
## 3 BRS Guamirim 42 3 G_42_R3 0
## 4 BRS Guamirim 42 4 G_42_R4 0.05
## 5 BRS Guamirim 108 1 G_108_R1 0.2
## 6 BRS Guamirim 108 2 G_108_R2 0.9
## 7 BRS Guamirim 108 3 G_108_R3 0.28
## 8 BRS Guamirim 108 4 G_108_R4 0.14
## 9 BRS Guamirim 110 1 G_110_R1 0.19
## 10 BRS Guamirim 110 2 G_110_R2 0.21
## # ... with 190 more rows
blast_data = gsheet2tbl("https://docs.google.com/spreadsheets/d/1KnJ9N8jqKPMjCt8jCLv7OqYL9hLyYeK8wkGEBmlGSVI/edit?usp=sharing") %>%
dplyr::select(code, city, region, position_wheat, host,species)
# blast_data
sev_data = full_join(sev, blast_data, by = "code") %>%
mutate(n = seq(1:1123)) %>%
filter(n<201) %>%
dplyr::select(-n) %>%
dplyr::select(pic_name, sev)
# sev_data
length(unique(sev$pic_name))
## [1] 200
all_data = box %>%
separate(leaf, into = c("pic_name", "jpg"), sep = ".jpg") %>%
# separate(pic_name, into = c("hh", "isolate","repp"), sep = "_", remove = F) %>%
dplyr::select(-jpg) %>%
full_join(sev_data) %>%
# filter(sev>0) %>%
mutate(sev = case_when(sev==0 ~0.01,
sev >0 ~sev)) %>%
filter(!is.na(index)) %>%
mutate(sev = sev)
## Joining, by = "pic_name"
length(unique(all_data$pic_name))
## [1] 200
# length(unique(box$leaf))
summary(sev$sev)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.6375 49.6250 44.0737 76.0050 99.9300
hist_sev_WLB = sev %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "black", bins = 20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"),
axis.title.y = element_text(size=8))
hist_sev_WLB
## Warning: Removed 2 rows containing missing values (geom_bar).
EX.L1<-stack(paste("./pics/01-Wheat_leaf_blast/","G_758_R2.jpg",sep = ""))
EX.L1<-aggregate(EX.L1, fact=10)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
## [1] "3 layers available"
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
## [1] "3 layers available"
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=180, cropAbove=T, plot = F)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
## [1] "3 layers available"
# plot(EX.L4$NGRDI)
rgb_fig_wlb = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"))
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
gli_fig_wlb = as.data.frame(EX.L4$VARI, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = VARI))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))
# plot_grid(rgb_fig, gli_fig, axis = "b",
# rel_widths = c(0.9,1),
# labels = c("RGB", "NGRDI"),
# scale = 0.90)
rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB
## Warning: Removed 2 rows containing missing values (geom_bar).
# ggsave("figs/leaf_gli_wlb.png",dpi = 600, height = 4, width =12)
rgb_gg = all_data %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg = all_data %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg,
nrow = 2,
rel_heights = c(0.5,1))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_WLB.png", dpi = 500, height = 8, width = 10)
cor_wlb = all_data %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev, method = "spearman")$estimate,3),
P_value = cor.test(mean,sev, method = "spearman")$p.value) %>%
arrange(-cor)
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## `summarise()` ungrouping output (override with `.groups` argument)
cor_wlb
## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 SCI 0.867 1.14e-61
## 2 HI 0.823 1.70e-50
## 3 Blue 0.822 2.70e-50
## 4 HUE 0.752 9.44e-38
## 5 Red 0.666 5.11e-27
## 6 BI 0.643 1.12e-24
## 7 gray 0.59 4.16e-20
## 8 BGI 0.475 1.22e-12
## 9 Green 0.436 1.07e-10
## 10 SI 0.382 2.33e- 8
## 11 NGRDI -0.867 1.14e-61
## 12 VARI -0.87 1.06e-62
## 13 GLI -0.886 4.53e-68
all_data_spread = all_data %>%
# mutate(nn=1:length(all_data$pic_name)) %>%
pivot_wider(id_col = c(pic_name,sev),
names_from = index,
values_from = mean)
all_data_spread
## # A tibble: 200 x 15
## pic_name sev BGI BI Blue GLI gray Green HI HUE NGRDI
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 G_108_R1 0.2 0.535 107. 71.7 0.193 118. 131. 0.218 -1.56 0.0970
## 2 G_108_R2 0.9 0.646 103. 79.2 0.148 112. 122. 0.0459 -1.50 0.0872
## 3 G_108_R3 0.28 0.678 99.3 79.4 0.137 107. 116. -0.0920 -1.55 0.0858
## 4 G_108_R4 0.14 0.649 104. 79.8 0.148 112. 123. -0.0500 -1.56 0.0885
## 5 G_110_R1 0.19 0.651 102. 79.3 0.148 111. 122. 0.0730 -1.56 0.0903
## 6 G_110_R2 0.21 0.638 104. 79.1 0.151 113. 124. 0.0666 -1.56 0.0867
## 7 G_110_R3 0.3 0.645 107. 81.9 0.144 116. 127. 0.112 -1.56 0.0797
## 8 G_110_R4 0.08 0.694 98.1 79.7 0.133 105. 115. -0.164 -1.56 0.0887
## 9 G_112_R1 0.01 0.645 104. 80.1 0.150 113. 124. -0.00156 -1.56 0.0902
## 10 G_112_R2 0.06 0.690 99.4 80.5 0.134 107. 116. -0.170 -1.56 0.0874
## # ... with 190 more rows, and 4 more variables: Red <dbl>, SCI <dbl>, SI <dbl>,
## # VARI <dbl>
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)
## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 52.
## There were 13 predictors of which 13 had non-zero influence.
sqrt(min(gbm.fit$cv.error))
## [1] 9.034891
gbm.perf(gbm.fit, method = "cv")
## [1] 52
# find index for n trees with minimum CV error
min_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])
## [1] 9.034891
# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )
## Using 52 trees...
sqrt(mean(((pred)-all_data_spread$sev[-train])^2))
## [1] 11.15379
CCC((pred), all_data_spread$sev[-train])$rho.c$est
## [1] 0.9461264
plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)
## [1] 192
# randomize data
set.seed(1234)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.80*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray +Red+Green+Blue + SI + SCI, #<<<<<
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC(pred, all_data_spread$sev[-train])$rho.c$est#<<<<<
}
## Using 5000 trees...
## Using 606 trees...
## Using 54 trees...
## Using 20 trees...
## Using 3601 trees...
## Using 323 trees...
## Using 29 trees...
## Using 9 trees...
## Using 3242 trees...
## Using 328 trees...
## Using 36 trees...
## Using 19 trees...
## Using 3225 trees...
## Using 331 trees...
## Using 39 trees...
## Using 7 trees...
## Using 4999 trees...
## Using 1013 trees...
## Using 58 trees...
## Using 31 trees...
## Using 3612 trees...
## Using 323 trees...
## Using 31 trees...
## Using 9 trees...
## Using 3378 trees...
## Using 328 trees...
## Using 31 trees...
## Using 71 trees...
## Using 3378 trees...
## Using 328 trees...
## Using 31 trees...
## Using 71 trees...
## Using 4999 trees...
## Using 945 trees...
## Using 91 trees...
## Using 23 trees...
## Using 4766 trees...
## Using 550 trees...
## Using 34 trees...
## Using 14 trees...
## Using 4766 trees...
## Using 550 trees...
## Using 34 trees...
## Using 14 trees...
## Using 4766 trees...
## Using 550 trees...
## Using 34 trees...
## Using 14 trees...
## Using 4995 trees...
## Using 591 trees...
## Using 50 trees...
## Using 27 trees...
## Using 3610 trees...
## Using 385 trees...
## Using 38 trees...
## Using 11 trees...
## Using 3304 trees...
## Using 5000 trees...
## Using 34 trees...
## Using 69 trees...
## Using 3205 trees...
## Using 4970 trees...
## Using 32 trees...
## Using 7 trees...
## Using 4995 trees...
## Using 938 trees...
## Using 2711 trees...
## Using 375 trees...
## Using 3493 trees...
## Using 336 trees...
## Using 31 trees...
## Using 53 trees...
## Using 3349 trees...
## Using 334 trees...
## Using 31 trees...
## Using 11 trees...
## Using 3349 trees...
## Using 324 trees...
## Using 31 trees...
## Using 11 trees...
## Using 4996 trees...
## Using 789 trees...
## Using 50 trees...
## Using 446 trees...
## Using 3659 trees...
## Using 389 trees...
## Using 38 trees...
##
## Using 38 trees...
## Using 3659 trees...
## Using 389 trees...
## Using 38 trees...
##
## Using 38 trees...
## Using 3659 trees...
## Using 389 trees...
## Using 38 trees...
##
## Using 38 trees...
## Using 4999 trees...
## Using 739 trees...
## Using 91 trees...
## Using 27 trees...
## Using 3636 trees...
## Using 391 trees...
## Using 29 trees...
## Using 82 trees...
## Using 3606 trees...
## Using 4988 trees...
## Using 1606 trees...
## Using 70 trees...
## Using 3527 trees...
## Using 5000 trees...
## Using 1913 trees...
## Using 12 trees...
## Using 4999 trees...
## Using 739 trees...
## Using 54 trees...
## Using 27 trees...
## Using 3648 trees...
## Using 4999 trees...
## Using 4511 trees...
## Using 11 trees...
## Using 3653 trees...
## Using 4999 trees...
## Using 4985 trees...
## Using 13 trees...
## Using 3540 trees...
## Using 4967 trees...
## Using 4417 trees...
## Using 13 trees...
## Using 5000 trees...
## Using 630 trees...
## Using 3637 trees...
## Using 28 trees...
## Using 3450 trees...
## Using 336 trees...
## Using 29 trees...
## Using 12 trees...
## Using 3378 trees...
## Using 341 trees...
## Using 29 trees...
## Using 10 trees...
## Using 3378 trees...
## Using 341 trees...
## Using 29 trees...
## Using 10 trees...
## Using 5000 trees...
## Using 1017 trees...
## Using 122 trees...
## Using 130 trees...
## Using 4663 trees...
## Using 456 trees...
## Using 48 trees...
## Using 10 trees...
## Using 3029 trees...
## Using 378 trees...
## Using 35 trees...
## Using 9 trees...
## Using 3539 trees...
## Using 301 trees...
## Using 27 trees...
## Using 9 trees...
## Using 5000 trees...
## Using 4997 trees...
## Using 953 trees...
## Using 28 trees...
## Using 3651 trees...
## Using 5000 trees...
## Using 4990 trees...
## Using 4906 trees...
## Using 3384 trees...
## Using 337 trees...
## Using 5000 trees...
## Using 10 trees...
## Using 3367 trees...
## Using 343 trees...
## Using 33 trees...
## Using 142 trees...
## Using 4999 trees...
## Using 622 trees...
## Using 90 trees...
## Using 20 trees...
## Using 3456 trees...
## Using 350 trees...
## Using 43 trees...
## Using 13 trees...
## Using 3195 trees...
## Using 318 trees...
## Using 32 trees...
## Using 10 trees...
## Using 3220 trees...
## Using 320 trees...
## Using 31 trees...
## Using 10 trees...
best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par
## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.01 5 5 0.80 4988
## 2 0.10 5 5 0.50 36
## 3 0.30 5 5 0.50 19
## 4 0.01 6 5 0.80 5000
## 5 0.30 3 10 0.80 11
## 6 0.01 6 5 0.50 331
## 7 0.01 5 5 0.65 5000
## 8 0.01 6 5 0.65 4970
## 9 0.10 5 10 0.80 4985
## 10 0.30 5 10 1.00 10
## min_RMSE CCC
## 1 10.83493 0.9731869
## 2 10.88780 0.9730593
## 3 11.04311 0.9729481
## 4 10.70554 0.9722267
## 5 10.84073 0.9716871
## 6 10.34085 0.9716634
## 7 10.62293 0.9713993
## 8 10.52067 0.9713708
## 9 10.54148 0.9711163
## 10 11.28268 0.9709846
# gbm.tune$fit
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+Red+Green+Blue+gray+SI+SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
par(mar = c(5, 8, 1, 1))
summary_gbm_wlb = summary(
gbm.fit.final,
cBars = 15,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_wlb = summary_gbm_wlb %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_wlb
ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)
Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "NGRDI", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( NGRDI,(yhat)))+
geom_line()
LIME
model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}
# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
# predict values for test data
pred <- predict(gbm.fit.final,
n.trees = gbm.fit.final$n.trees,
all_data_spread[-train,])
# results
caret::RMSE((pred), all_data_spread[-train,]$sev)
## [1] 7.852842
CCC((pred), all_data_spread$sev[-train])$rho.c$est
## [1] 0.9733397
cor((pred), all_data_spread$sev[-train])^2
## [1] 0.9491022
accuracy_wlb =data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_wlb
## RMSE r s.shift l.shift C.b CCC CIS
## 1 7.852842 0.9742187 1.030742 0.02982214 0.9990977 0.9733397 0.95, 0.99
# plot_grid(
conc_wlb = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual,predict))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)
## `geom_smooth()` using formula 'y ~ x'
pics<-list.files("./pics/01-soybean-rust-bg-blue")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-soybean-rust-bg-blue/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=100, cropAbove=T, plot = F)
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_SBR.txt")
box = read.table("data/indexes_SBR.txt")
sbr_load = gsheet2tbl("https://docs.google.com/spreadsheets/d/13TVKBQgfCAr7UGie_LHTF_kwPHC1XI_AkLKoRYtjPrQ/edit?usp=sharing")
all_data_sbr = box %>%
separate(leaf, into=c("file","format"), sep ="_") %>%
dplyr::select(-format) %>%
full_join(sbr_load) %>%
na.omit()
## Joining, by = "file"
length(unique(all_data_sbr$file))
## [1] 203
head(all_data_sbr)
## index mean std Q25 Q50 Q75 file
## 1 BGI 0.3997101 0.13599078 0.3234259 0.3843265 0.4506906 Ferrugem 1
## 2 BI 87.8460326 14.91203193 77.1221560 87.0969043 98.8352819 Ferrugem 1
## 3 Blue 41.0937912 11.80079235 33.5510204 40.1326531 46.7959184 Ferrugem 1
## 4 GLI 0.1959277 0.06355385 0.1562426 0.1952057 0.2354938 Ferrugem 1
## 5 gray 96.1659570 15.23897816 85.5722755 95.7584898 107.3897857 Ferrugem 1
## 6 Green 104.4390983 13.30553965 95.8163265 104.3571429 113.8163265 Ferrugem 1
## sev
## 1 39.7
## 2 39.7
## 3 39.7
## 4 39.7
## 5 39.7
## 6 39.7
summary(all_data_sbr$sev)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.04 5.52 20.10 25.11 38.07 89.67
hist_sev_sbr = all_data_sbr %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean) %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "black", bins = 20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"),
axis.title.y = element_text(size=8))
EX.L1<-stack(paste("./pics/01-soybean-rust-bg-blue/","Ferrugem 2_Median.jpg",sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
## [1] "3 layers available"
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
## [1] "3 layers available"
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=100, cropAbove=T, plot = F)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
## [1] "3 layers available"
# plot(EX.L4$HUE)
rgb_fig_sbr = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"))
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
gli_fig_sbr = as.data.frame(EX.L4$HUE, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = HUE))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))
rgb_fig_sbr + gli_fig_sbr + hist_sev_sbr #+
## Warning: Removed 2 rows containing missing values (geom_bar).
# rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB+
# plot_layout(widths = c(1, 1, 1),
# heights = c(1,1))
# ggsave("figs/leaf_gli.png",dpi = 600, height = 6, width =10)
rgb_gg_sbr = all_data_sbr %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg_sbr
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg_sbr = all_data_sbr %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg_sbr
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg_sbr,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg_sbr,
nrow = 2,
rel_heights = c(0.5,1))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_SBR.png", dpi = 500, height = 8, width = 10)
cor_sbr = all_data_sbr %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev, method = "spearman")$estimate,3),
P_value = cor.test(mean,sev, method = "spearman")$p.value) %>%
arrange(-cor)
## `summarise()` ungrouping output (override with `.groups` argument)
cor_sbr
## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 HUE 0.995 0.
## 2 SCI 0.946 0.
## 3 HI 0.906 0.
## 4 Red 0.848 0.
## 5 SI 0.823 0.
## 6 BI 0.745 0.
## 7 gray 0.71 0.
## 8 Green 0.582 0.
## 9 Blue -0.383 2.26e- 8
## 10 GLI -0.462 3.93e-12
## 11 BGI -0.617 0.
## 12 VARI -0.88 0.
## 13 NGRDI -0.946 0.
all_data_spread = all_data_sbr %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean)
all_data_spread
## # A tibble: 203 x 15
## file sev BGI BI Blue GLI gray Green HI HUE NGRDI Red
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Ferr~ 39.7 0.400 87.8 41.1 0.196 96.2 104. 0.663 -0.263 0.0320 101.
## 2 Ferr~ 18.6 0.379 84.7 39.7 0.231 94.0 105. 0.565 -0.953 0.0711 92.9
## 3 Ferr~ 26.4 0.503 90.3 53.1 0.165 98.7 107. 0.746 -0.668 0.0372 100.
## 4 Ferr~ 2.33 0.554 81.2 55.1 0.186 89.3 99.7 0.204 -1.50 0.101 82.0
## 5 Ferr~ 84.7 0.434 113. 51.8 0.116 121. 123. 1.62 1.23 -0.0713 142.
## 6 Ferr~ 4.08 0.505 89.3 55.5 0.196 98.7 110. 0.319 -1.43 0.0876 93.0
## 7 Ferr~ 1.1 0.580 80.8 57.5 0.182 88.6 99.0 0.0999 -1.54 0.108 80.0
## 8 Ferr~ 48.5 0.506 107. 60.3 0.133 116. 122. 1.19 0.0521 -0.0118 125.
## 9 Ferr~ 2.62 0.574 82.6 57.4 0.173 90.6 100. 0.127 -1.49 0.0900 84.1
## 10 Ferr~ 6.92 0.426 112. 57.7 0.199 124. 136. 0.678 -1.31 0.0465 124.
## # ... with 193 more rows, and 3 more variables: SCI <dbl>, SI <dbl>, VARI <dbl>
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)
## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 942.
## There were 13 predictors of which 13 had non-zero influence.
sqrt(min(gbm.fit$cv.error))
## [1] 4.02846
gbm.perf(gbm.fit, method = "cv")
## [1] 942
# find index for n trees with minimum CV error
min_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])
## [1] 4.02846
# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )
## Using 942 trees...
sqrt(mean(((pred)-all_data_spread$sev[-train])^2))
## [1] 3.398297
CCC((pred), all_data_spread$sev[-train])$rho.c$est
## [1] 0.9897837
plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)
## [1] 192
# randomize data
set.seed(123)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.8*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray +Red+Green+Blue + SI + SCI, #<<<<<
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC(pred, all_data_spread$sev[-train])$rho.c$est#<<<<<
}
## Using 5000 trees...
## Using 2636 trees...
## Using 228 trees...
## Using 57 trees...
## Using 4999 trees...
## Using 1734 trees...
## Using 178 trees...
## Using 357 trees...
## Using 5000 trees...
## Using 1797 trees...
## Using 153 trees...
## Using 126 trees...
## Using 5000 trees...
## Using 1797 trees...
## Using 153 trees...
## Using 76 trees...
## Using 5000 trees...
## Using 4969 trees...
## Using 591 trees...
## Using 297 trees...
## Using 5000 trees...
## Using 4916 trees...
## Using 485 trees...
## Using 70 trees...
## Using 5000 trees...
## Using 4896 trees...
## Using 657 trees...
## Using 118 trees...
## Using 5000 trees...
## Using 4896 trees...
## Using 657 trees...
## Using 118 trees...
## Using 5000 trees...
## Using 3278 trees...
## Using 394 trees...
## Using 68 trees...
## Using 5000 trees...
## Using 2055 trees...
## Using 156 trees...
## Using 63 trees...
## Using 5000 trees...
## Using 2055 trees...
## Using 156 trees...
## Using 63 trees...
## Using 5000 trees...
## Using 2055 trees...
## Using 156 trees...
## Using 63 trees...
## Using 5000 trees...
## Using 1650 trees...
## Using 131 trees...
## Using 105 trees...
## Using 4995 trees...
## Using 1904 trees...
## Using 98 trees...
## Using 27 trees...
## Using 5000 trees...
## Using 1576 trees...
## Using 132 trees...
## Using 84 trees...
## Using 5000 trees...
## Using 1579 trees...
## Using 132 trees...
## Using 10 trees...
## Using 5000 trees...
## Using 4943 trees...
## Using 576 trees...
## Using 134 trees...
## Using 5000 trees...
## Using 4988 trees...
## Using 489 trees...
## Using 212 trees...
## Using 5000 trees...
## Using 4083 trees...
## Using 298 trees...
## Using 99 trees...
## Using 5000 trees...
## Using 4129 trees...
## Using 424 trees...
## Using 214 trees...
## Using 5000 trees...
## Using 3608 trees...
## Using 439 trees...
## Using 133 trees...
## Using 5000 trees...
## Using 2813 trees...
## Using 449 trees...
## Using 63 trees...
## Using 5000 trees...
## Using 2813 trees...
## Using 449 trees...
## Using 59 trees...
## Using 5000 trees...
## Using 2813 trees...
## Using 449 trees...
## Using 59 trees...
## Using 5000 trees...
## Using 1601 trees...
## Using 125 trees...
## Using 53 trees...
## Using 5000 trees...
## Using 1148 trees...
## Using 78 trees...
## Using 15 trees...
## Using 5000 trees...
## Using 896 trees...
## Using 80 trees...
## Using 23 trees...
## Using 5000 trees...
## Using 1161 trees...
## Using 105 trees...
## Using 17 trees...
## Using 5000 trees...
## Using 4708 trees...
## Using 728 trees...
## Using 428 trees...
## Using 5000 trees...
## Using 4236 trees...
## Using 374 trees...
## Using 118 trees...
## Using 5000 trees...
## Using 3092 trees...
## Using 301 trees...
## Using 102 trees...
## Using 5000 trees...
## Using 3103 trees...
## Using 263 trees...
## Using 109 trees...
## Using 5000 trees...
## Using 4714 trees...
## Using 655 trees...
## Using 247 trees...
## Using 5000 trees...
## Using 4756 trees...
## Using 499 trees...
## Using 127 trees...
## Using 5000 trees...
## Using 3402 trees...
## Using 292 trees...
## Using 130 trees...
## Using 5000 trees...
## Using 3402 trees...
## Using 292 trees...
## Using 130 trees...
## Using 5000 trees...
## Using 3297 trees...
## Using 355 trees...
## Using 1401 trees...
## Using 5000 trees...
## Using 4600 trees...
## Using 612 trees...
## Using 20 trees...
## Using 4999 trees...
## Using 1109 trees...
## Using 93 trees...
## Using 33 trees...
## Using 5000 trees...
## Using 565 trees...
## Using 97 trees...
## Using 27 trees...
## Using 5000 trees...
## Using 4994 trees...
## Using 1345 trees...
## Using 297 trees...
## Using 5000 trees...
## Using 2565 trees...
## Using 271 trees...
## Using 133 trees...
## Using 5000 trees...
## Using 2912 trees...
## Using 235 trees...
## Using 77 trees...
## Using 5000 trees...
## Using 3010 trees...
## Using 246 trees...
## Using 104 trees...
## Using 5000 trees...
## Using 4997 trees...
## Using 738 trees...
## Using 227 trees...
## Using 5000 trees...
## Using 4998 trees...
## Using 585 trees...
## Using 109 trees...
## Using 4999 trees...
## Using 4259 trees...
## Using 385 trees...
## Using 149 trees...
## Using 5000 trees...
## Using 3530 trees...
## Using 400 trees...
## Using 202 trees...
best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par
## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.010 3 5 0.80 1148
## 2 0.010 5 5 1.00 1109
## 3 0.100 6 5 1.00 97
## 4 0.100 5 5 1.00 93
## 5 0.010 3 5 1.00 4600
## 6 0.010 1 5 0.80 1601
## 7 0.010 1 5 0.65 1650
## 8 0.001 3 5 0.65 4995
## 9 0.010 6 5 0.80 1161
## 10 0.001 3 5 0.80 5000
## min_RMSE CCC
## 1 2.270680 0.9958594
## 2 2.002059 0.9956714
## 3 1.975201 0.9955144
## 4 2.055946 0.9955131
## 5 2.157053 0.9955040
## 6 2.266556 0.9954981
## 7 2.375012 0.9954573
## 8 2.488595 0.9954234
## 9 2.073971 0.9953818
## 10 2.302626 0.9953742
# gbm.tune$fit
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+Red+Green+Blue+gray+SI+SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
par(mar = c(5, 8, 1, 1))
summary_gbm_sbr = summary(
gbm.fit.final,
cBars = 15,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_sbr = summary_gbm_sbr %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_sbr
# ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)
Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "HUE", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( HUE,(yhat)))+
geom_line()
LIME
library(lime)
model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}
# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
# predict values for test data
pred <- predict(gbm.fit.final,
n.trees = gbm.fit.final$n.trees,
all_data_spread[-train,])
# results
caret::RMSE((pred), all_data_spread[-train,]$sev)
## [1] 1.926231
CCC(pred, all_data_spread$sev[-train])$rho.c$est
## [1] 0.9958621
cor(pred, all_data_spread$sev[-train])^2
## [1] 0.9921386
accuracy_sbr =data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_sbr
## RMSE r s.shift l.shift C.b CCC CIS
## 1 1.926231 0.9960615 0.9916907 -0.01819114 0.9997998 0.9958621 0.99, 1
conc_sbr = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual, predict ))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)
## `geom_smooth()` using formula 'y ~ x'
pics<-list.files("./pics/01-Xylella-tobacco-bg-white")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-Xylella-tobacco-bg-white/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=200, cropAbove=T, plot = F)
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_Xylella.txt")
box = read.table("data/indexes_Xylella.txt")
data_xy_load = read_csv("data_pics/01-Xylella-tobacco-severity.csv") %>%
mutate(file = as.character(File)) %>%
dplyr::select(-File)
##
## -- Column specification --------------------------------------------------------
## cols(
## File = col_double(),
## ImageJ = col_double(),
## LeafDoctor = col_double(),
## APSAssess = col_double()
## )
data_xy = box %>%
separate(leaf, into=c("file","format"), sep =".jpg") %>%
dplyr::select(-format) %>%
full_join(data_xy_load, by="file") %>%
mutate(sev=ImageJ)
data_xy
## index mean std Q25 Q50 Q75
## 1 BGI 5.436164e-01 0.060121706 5.121507e-01 5.339519e-01 5.541888e-01
## 2 BI 1.671294e+02 13.092581029 1.590453e+02 1.673032e+02 1.744013e+02
## 3 Blue 1.055687e+02 17.594940645 9.485714e+01 1.037959e+02 1.121020e+02
## 4 GLI 1.386614e-01 0.020247634 1.340011e-01 1.426077e-01 1.493713e-01
## 5 gray 1.815699e+02 13.150761073 1.733458e+02 1.820882e+02 1.895008e+02
## 6 Green 1.933107e+02 13.420773604 1.847347e+02 1.940408e+02 2.020408e+02
## 7 HI 8.683842e-01 0.102006173 8.046512e-01 8.494624e-01 9.244940e-01
## 8 HUE -1.231244e+00 0.938461283 -1.564719e+00 -1.562570e+00 -1.553532e+00
## 9 NGRDI 1.513704e-02 0.011610581 8.698471e-03 1.786423e-02 2.269336e-02
## 10 Red 1.874974e+02 12.336910350 1.802041e+02 1.880408e+02 1.948163e+02
## 11 SCI -1.513704e-02 0.011610581 -2.269336e-02 -1.786423e-02 -8.698471e-03
## 12 SI 2.835890e-01 0.047290707 2.687086e-01 2.891532e-01 3.124621e-01
## 13 VARI 2.097451e-02 0.016012463 1.176282e-02 2.450909e-02 3.152252e-02
## 14 BGI 5.232702e-01 0.064259254 4.901864e-01 5.117849e-01 5.343837e-01
## 15 BI 1.617949e+02 13.149662642 1.538317e+02 1.616828e+02 1.685547e+02
## 16 Blue 9.936791e+01 18.019025309 8.900000e+01 9.702041e+01 1.052194e+02
## 17 GLI 1.489379e-01 0.022855570 1.426525e-01 1.522061e-01 1.609541e-01
## 18 gray 1.763900e+02 13.065001780 1.685468e+02 1.765641e+02 1.836820e+02
## 19 Green 1.888874e+02 13.024434760 1.812449e+02 1.894694e+02 1.965714e+02
## 20 HI 8.288865e-01 0.108060562 7.430341e-01 8.064516e-01 8.758588e-01
## 21 HUE -1.305940e+00 0.838101317 -1.565441e+00 -1.564433e+00 -1.559984e+00
## 22 NGRDI 2.076509e-02 0.013190269 1.435438e-02 2.346119e-02 2.917836e-02
## 23 Red 1.812212e+02 12.792847953 1.736122e+02 1.815612e+02 1.886735e+02
## 24 SCI -2.076509e-02 0.013190269 -2.917836e-02 -2.346119e-02 -1.435438e-02
## 25 SI 2.962883e-01 0.050157294 2.822797e-01 3.044717e-01 3.244522e-01
## 26 VARI 2.834054e-02 0.017835753 1.980149e-02 3.213349e-02 3.987774e-02
## 27 BGI 4.770135e-01 0.082275350 4.382802e-01 4.640944e-01 4.856903e-01
## 28 BI 1.346668e+02 16.903593814 1.247420e+02 1.328121e+02 1.413769e+02
## 29 Blue 7.849852e+01 21.629501426 6.740816e+01 7.530612e+01 8.242857e+01
## 30 GLI 1.841680e-01 0.032885559 1.770874e-01 1.896025e-01 2.013689e-01
## 31 gray 1.483695e+02 16.606670990 1.383943e+02 1.469750e+02 1.557476e+02
## 32 Green 1.625577e+02 15.721708271 1.530357e+02 1.617551e+02 1.703112e+02
## 33 HI 6.317971e-01 0.081442239 5.531797e-01 6.365651e-01 6.761815e-01
## 34 HUE -1.566068e+00 0.002466480 -1.567126e+00 -1.566629e+00 -1.565890e+00
## 35 NGRDI 5.079765e-02 0.014373767 4.323811e-02 5.162627e-02 6.232151e-02
## 36 Red 1.471550e+02 17.493227737 1.365714e+02 1.449184e+02 1.555765e+02
## 37 SCI -5.079765e-02 0.014373767 -6.232151e-02 -5.162627e-02 -4.323811e-02
## 38 SI 3.130982e-01 0.060454942 3.008149e-01 3.208377e-01 3.440621e-01
## 39 VARI 6.729878e-02 0.018001981 5.792041e-02 6.774116e-02 8.341990e-02
## 40 BGI 5.471168e-01 0.064567155 5.118101e-01 5.328164e-01 5.587819e-01
## 41 BI 1.626126e+02 13.429799977 1.551476e+02 1.613388e+02 1.691920e+02
## 42 Blue 1.033741e+02 18.416521184 9.302041e+01 9.967347e+01 1.090000e+02
## 43 GLI 1.380349e-01 0.023116581 1.296631e-01 1.415430e-01 1.522815e-01
## 44 gray 1.765774e+02 13.240406905 1.692520e+02 1.757616e+02 1.836343e+02
## 45 Green 1.879669e+02 13.149076689 1.804898e+02 1.875714e+02 1.952449e+02
## 46 HI 8.632352e-01 0.120741343 7.977528e-01 8.528022e-01 9.247863e-01
## 47 HUE -1.178044e+00 1.005955360 -1.564537e+00 -1.561666e+00 -1.552260e+00
## 48 NGRDI 1.582729e-02 0.014003198 8.299927e-03 1.680000e-02 2.467409e-02
## 49 Red 1.821276e+02 13.014208369 1.748571e+02 1.815306e+02 1.895306e+02
## 50 SCI -1.582729e-02 0.014003198 -2.467409e-02 -1.680000e-02 -8.299927e-03
## 51 SI 2.803030e-01 0.049074718 2.674682e-01 2.903750e-01 3.087919e-01
## 52 VARI 2.191157e-02 0.019195066 1.158766e-02 2.317333e-02 3.360512e-02
## 53 BGI 6.397902e-01 0.057576557 6.033778e-01 6.395914e-01 6.702550e-01
## 54 BI 1.883111e+02 15.966699694 1.786307e+02 1.888041e+02 1.989763e+02
## 55 Blue 1.349047e+02 19.447790916 1.220816e+02 1.347551e+02 1.473673e+02
## 56 GLI 9.997205e-02 0.022113827 8.811560e-02 9.980354e-02 1.142050e-01
## 57 gray 2.013672e+02 16.127278310 1.921515e+02 2.021308e+02 2.119078e+02
## 58 Green 2.101206e+02 16.487520281 2.018980e+02 2.112551e+02 2.204898e+02
## 59 HI 9.952901e-01 0.203793834 9.097048e-01 9.727264e-01 1.007014e+00
## 60 HUE -6.338601e-01 1.346936550 -1.555699e+00 -1.520522e+00 1.359702e+00
## 61 NGRDI 1.230542e-03 0.016915886 -5.605370e-04 2.476373e-03 8.748221e-03
## 62 Red 2.095227e+02 15.755228570 1.998367e+02 2.101735e+02 2.200612e+02
## 63 SCI -1.230542e-03 0.016915886 -8.748221e-03 -2.476373e-03 5.605370e-04
## 64 SI 2.199965e-01 0.041622674 1.968736e-01 2.194891e-01 2.451591e-01
## 65 VARI 1.781766e-03 0.024176415 -8.544055e-04 3.614236e-03 1.267687e-02
## 66 BGI 5.820188e-01 0.067428110 5.315358e-01 5.693426e-01 6.175964e-01
## 67 BI 1.772043e+02 16.401507923 1.661700e+02 1.758584e+02 1.867000e+02
## 68 Blue 1.183991e+02 20.538575261 1.033878e+02 1.151020e+02 1.297755e+02
## 69 GLI 1.248815e-01 0.029034530 1.071104e-01 1.293797e-01 1.484303e-01
## 70 gray 1.914034e+02 16.216391501 1.811177e+02 1.903582e+02 2.006643e+02
## 71 Green 2.024200e+02 15.776128292 1.937755e+02 2.017143e+02 2.111020e+02
## 72 HI 8.999821e-01 0.206456865 7.832061e-01 8.690452e-01 9.447821e-01
## 73 HUE -1.121452e+00 1.066487327 -1.565103e+00 -1.561004e+00 -1.546838e+00
## 74 NGRDI 1.236663e-02 0.019391600 5.042075e-03 1.395027e-02 2.521696e-02
## 75 Red 1.976099e+02 17.276915595 1.853112e+02 1.964898e+02 2.083673e+02
## 76 SCI -1.236663e-02 0.019391600 -2.521696e-02 -1.395027e-02 -5.042075e-03
## 77 SI 2.550979e-01 0.045179189 2.323478e-01 2.627929e-01 2.850668e-01
## 78 VARI 1.709632e-02 0.027170859 7.449380e-03 1.972998e-02 3.474857e-02
## 79 BGI 5.085819e-01 0.065712411 4.773217e-01 4.939795e-01 5.140404e-01
## 80 BI 1.445236e+02 12.891753106 1.374060e+02 1.428794e+02 1.488367e+02
## 81 Blue 8.702637e+01 17.652175236 7.822449e+01 8.355102e+01 8.957143e+01
## 82 GLI 1.571989e-01 0.024133703 1.494502e-01 1.607931e-01 1.712432e-01
## 83 gray 1.579929e+02 12.709072914 1.507976e+02 1.566197e+02 1.630469e+02
## 84 Green 1.700325e+02 12.655048196 1.627347e+02 1.690612e+02 1.759796e+02
## 85 HI 7.936470e-01 0.101355477 7.169132e-01 7.893328e-01 8.598902e-01
## 86 HUE -1.466058e+00 0.523798340 -1.565577e+00 -1.563556e+00 -1.559642e+00
## 87 NGRDI 2.601853e-02 0.013494652 1.692368e-02 2.608425e-02 3.531347e-02
## 88 Red 1.614142e+02 12.201318919 1.543061e+02 1.604082e+02 1.664898e+02
## 89 SCI -2.601853e-02 0.013494652 -3.531347e-02 -2.608425e-02 -1.692368e-02
## 90 SI 3.046540e-01 0.050377029 2.973605e-01 3.144625e-01 3.302657e-01
## 91 VARI 3.514187e-02 0.017979049 2.316398e-02 3.531491e-02 4.770609e-02
## 92 BGI 5.140943e-01 0.066878803 4.825597e-01 5.055288e-01 5.262627e-01
## 93 BI 1.206068e+02 15.753601285 1.125480e+02 1.169682e+02 1.243959e+02
## 94 Blue 7.661937e+01 17.705114033 6.989796e+01 7.322449e+01 7.700000e+01
## 95 GLI 1.916382e-01 0.028509055 1.899393e-01 1.973042e-01 2.040239e-01
## 96 gray 1.331735e+02 15.668948939 1.246965e+02 1.294918e+02 1.377841e+02
## 97 Green 1.480769e+02 14.975951780 1.396122e+02 1.446122e+02 1.533469e+02
## 98 HI 3.599747e-01 0.104268323 2.922038e-01 3.497448e-01 4.250591e-01
## 99 HUE -1.567211e+00 0.001128685 -1.567569e+00 -1.567394e+00 -1.567210e+00
## 100 NGRDI 8.445326e-02 0.017164204 7.931397e-02 8.714782e-02 9.400915e-02
## 101 Red 1.254772e+02 17.404673932 1.156122e+02 1.213878e+02 1.309796e+02
## 102 SCI -8.445326e-02 0.017164204 -9.400915e-02 -8.714782e-02 -7.931397e-02
## 103 SI 2.457563e-01 0.047373487 2.245343e-01 2.455025e-01 2.727635e-01
## 104 VARI 1.166940e-01 0.023432053 1.075013e-01 1.201967e-01 1.304114e-01
## 105 BGI 4.876649e-01 0.075177217 4.522709e-01 4.716773e-01 4.942060e-01
## 106 BI 1.293983e+02 17.900713893 1.187963e+02 1.263242e+02 1.353274e+02
## 107 Blue 7.713667e+01 21.125797394 6.622449e+01 7.298980e+01 7.975510e+01
## 108 GLI 1.826781e-01 0.030965041 1.772256e-01 1.887532e-01 1.986924e-01
## 109 gray 1.425269e+02 17.886501674 1.315521e+02 1.397158e+02 1.494310e+02
## 110 Green 1.563000e+02 17.484522103 1.453061e+02 1.538469e+02 1.641939e+02
## 111 HI 5.989870e-01 0.098148849 5.285039e-01 5.945295e-01 6.481746e-01
## 112 HUE -1.564649e+00 0.051323937 -1.567106e+00 -1.566624e+00 -1.565615e+00
## 113 NGRDI 5.457846e-02 0.016582721 4.544022e-02 5.596591e-02 6.660335e-02
## 114 Red 1.404189e+02 18.544979568 1.289592e+02 1.375306e+02 1.477143e+02
## 115 SCI -5.457846e-02 0.016582721 -6.660335e-02 -5.596591e-02 -4.544022e-02
## 116 SI 2.988513e-01 0.054816385 2.849264e-01 3.075378e-01 3.285267e-01
## 117 VARI 7.304889e-02 0.021377644 6.082658e-02 7.385281e-02 8.893299e-02
## 118 BGI 6.027203e-01 0.058716015 5.723945e-01 5.950962e-01 6.254052e-01
## 119 BI 1.761344e+02 15.816844788 1.683357e+02 1.775403e+02 1.855630e+02
## 120 Blue 1.184159e+02 18.482792674 1.081429e+02 1.177959e+02 1.268980e+02
## 121 GLI 1.017298e-01 0.028620014 8.394042e-02 1.022331e-01 1.220593e-01
## 122 gray 1.886308e+02 16.616136011 1.811637e+02 1.905397e+02 1.987039e+02
## 123 Green 1.958948e+02 18.284371202 1.886327e+02 1.984898e+02 2.073469e+02
## 124 HI 1.154198e+00 0.380323859 8.456439e-01 1.042006e+00 1.386065e+00
## 125 HUE 1.217992e-01 1.545815863 -1.560599e+00 1.536585e+00 1.567414e+00
## 126 NGRDI -1.370210e-02 0.034856111 -3.748638e-02 -4.133512e-03 1.398697e-02
## 127 Red 2.011410e+02 17.022548144 1.914694e+02 2.013265e+02 2.129592e+02
## 128 SCI 1.370210e-02 0.034856111 -1.398697e-02 4.133512e-03 3.748638e-02
## 129 SI 2.620369e-01 0.053865055 2.320727e-01 2.663788e-01 2.978484e-01
## 130 VARI -1.880448e-02 0.048624172 -5.247755e-02 -6.018349e-03 2.065343e-02
## 131 BGI 4.994713e-01 0.062777757 4.713489e-01 4.884036e-01 5.093375e-01
## 132 BI 1.497264e+02 13.170979189 1.423009e+02 1.498734e+02 1.565621e+02
## 133 Blue 8.963874e+01 16.491499847 8.120408e+01 8.761224e+01 9.475510e+01
## 134 GLI 1.691493e-01 0.023713622 1.631395e-01 1.729868e-01 1.810802e-01
## 135 gray 1.643772e+02 13.225405177 1.568870e+02 1.649127e+02 1.717896e+02
## 136 Green 1.785810e+02 12.990664628 1.712857e+02 1.795918e+02 1.863265e+02
## 137 HI 6.927919e-01 0.051306931 6.653543e-01 6.816098e-01 7.152369e-01
## 138 HUE -1.564074e+00 0.078314568 -1.566842e+00 -1.566518e+00 -1.565873e+00
## 139 NGRDI 3.993923e-02 0.008205029 3.532326e-02 4.186875e-02 4.538473e-02
## 140 Red 1.649876e+02 13.655044873 1.569592e+02 1.653673e+02 1.726939e+02
## 141 SCI -3.993923e-02 0.008205029 -4.538473e-02 -4.186875e-02 -3.532326e-02
## 142 SI 3.001807e-01 0.046054075 2.914037e-01 3.078610e-01 3.217996e-01
## 143 VARI 5.372709e-02 0.010172228 4.814663e-02 5.628613e-02 6.046925e-02
## 144 BGI 5.300425e-01 0.068896236 4.929369e-01 5.145007e-01 5.439166e-01
## 145 BI 1.496112e+02 12.531708671 1.424685e+02 1.478781e+02 1.540959e+02
## 146 Blue 9.188030e+01 17.952822086 8.239796e+01 8.789796e+01 9.583673e+01
## 147 GLI 1.381701e-01 0.025542847 1.272303e-01 1.428964e-01 1.547976e-01
## 148 gray 1.624231e+02 12.000969565 1.556090e+02 1.609982e+02 1.670893e+02
## 149 Green 1.722959e+02 11.286002785 1.661429e+02 1.711633e+02 1.766531e+02
## 150 HI 9.428877e-01 0.161228816 8.285921e-01 9.312235e-01 1.035431e+00
## 151 HUE -5.398159e-01 1.437685890 -1.561503e+00 -1.548424e+00 1.529215e+00
## 152 NGRDI 7.252632e-03 0.017531369 -4.178290e-03 8.006498e-03 2.006209e-02
## 153 Red 1.699368e+02 12.869922568 1.616531e+02 1.690408e+02 1.763265e+02
## 154 SCI -7.252632e-03 0.017531369 -2.006209e-02 -8.006498e-03 4.178290e-03
## 155 SI 3.029920e-01 0.052991791 2.897487e-01 3.124898e-01 3.317522e-01
## 156 VARI 9.889932e-03 0.023942800 -5.670858e-03 1.101950e-02 2.742532e-02
## 157 BGI 5.324968e-01 0.069526844 4.994106e-01 5.258065e-01 5.492049e-01
## 158 BI 1.135192e+02 16.596245862 1.057791e+02 1.106585e+02 1.164265e+02
## 159 Blue 7.456771e+01 18.740739102 6.738776e+01 7.153061e+01 7.530612e+01
## 160 GLI 1.866963e-01 0.030547644 1.814737e-01 1.907970e-01 2.004806e-01
## 161 gray 1.250420e+02 16.398548862 1.169126e+02 1.221848e+02 1.288145e+02
## 162 Green 1.388656e+02 15.643941717 1.306531e+02 1.361020e+02 1.435306e+02
## 163 HI 3.183277e-01 0.114943230 2.360965e-01 3.166827e-01 3.961790e-01
## 164 HUE -1.566710e+00 0.001284595 -1.567272e+00 -1.566934e+00 -1.566555e+00
## 165 NGRDI 8.693394e-02 0.018954893 8.092759e-02 8.887890e-02 9.770769e-02
## 166 Red 1.171477e+02 17.962551075 1.080000e+02 1.137959e+02 1.213265e+02
## 167 SCI -8.693394e-02 0.018954893 -9.770769e-02 -8.887890e-02 -8.092759e-02
## 168 SI 2.269589e-01 0.047345603 2.035358e-01 2.266784e-01 2.530682e-01
## 169 VARI 1.218013e-01 0.025982085 1.113648e-01 1.239330e-01 1.378939e-01
## 170 BGI 5.294524e-01 0.070994961 4.970554e-01 5.183500e-01 5.404671e-01
## 171 BI 1.198689e+02 16.040382459 1.119103e+02 1.168980e+02 1.226360e+02
## 172 Blue 7.708734e+01 19.007502082 6.926531e+01 7.340816e+01 7.796939e+01
## 173 GLI 1.738413e-01 0.029554148 1.682256e-01 1.773893e-01 1.880305e-01
## 174 gray 1.316147e+02 15.801279438 1.233271e+02 1.287510e+02 1.350395e+02
## 175 Green 1.443754e+02 15.292952874 1.356735e+02 1.417551e+02 1.486939e+02
## 176 HI 4.930155e-01 0.096269059 4.240023e-01 4.971246e-01 5.559973e-01
## 177 HUE -1.565788e+00 0.002360316 -1.566816e+00 -1.566074e+00 -1.565377e+00
## 178 NGRDI 6.380929e-02 0.016101037 5.486079e-02 6.456954e-02 7.530962e-02
## 179 Red 1.273525e+02 16.644651923 1.187653e+02 1.242245e+02 1.308980e+02
## 180 SCI -6.380929e-02 0.016101037 -7.530962e-02 -6.456954e-02 -5.486079e-02
## 181 SI 2.516587e-01 0.047862102 2.351059e-01 2.563707e-01 2.767461e-01
## 182 VARI 8.832271e-02 0.021400819 7.579152e-02 8.897186e-02 1.041362e-01
## 183 BGI 5.287628e-01 0.070768924 4.966605e-01 5.195187e-01 5.404123e-01
## 184 BI 1.203881e+02 15.185179492 1.135449e+02 1.174843e+02 1.226794e+02
## 185 Blue 7.804833e+01 18.447548041 7.097959e+01 7.475510e+01 7.853061e+01
## 186 GLI 1.822087e-01 0.029944429 1.788503e-01 1.865540e-01 1.953764e-01
## 187 gray 1.325029e+02 14.782043711 1.254898e+02 1.297281e+02 1.354214e+02
## 188 Green 1.464739e+02 13.942594373 1.395510e+02 1.439184e+02 1.499796e+02
## 189 HI 3.929090e-01 0.085102284 3.366584e-01 3.842606e-01 4.423213e-01
## 190 HUE -1.566065e+00 0.045254913 -1.567268e+00 -1.567029e+00 -1.566758e+00
## 191 NGRDI 7.719080e-02 0.015561213 7.316398e-02 8.037772e-02 8.565531e-02
## 192 Red 1.258368e+02 15.997912276 1.178367e+02 1.227347e+02 1.289796e+02
## 193 SCI -7.719080e-02 0.015561213 -8.565531e-02 -8.037772e-02 -7.316398e-02
## 194 SI 2.396596e-01 0.046485414 2.221421e-01 2.419291e-01 2.637740e-01
## 195 VARI 1.073379e-01 0.020453248 1.006270e-01 1.112730e-01 1.192763e-01
## 196 BGI 5.250583e-01 0.071932587 4.942271e-01 5.141938e-01 5.346963e-01
## 197 BI 1.191959e+02 15.958961955 1.109121e+02 1.158714e+02 1.217715e+02
## 198 Blue 7.605282e+01 19.294065268 6.783673e+01 7.234694e+01 7.728571e+01
## 199 GLI 1.740674e-01 0.029238551 1.689298e-01 1.777558e-01 1.873276e-01
## 200 gray 1.308820e+02 15.589922749 1.224805e+02 1.275846e+02 1.339535e+02
## 201 Green 1.434513e+02 14.817460119 1.351224e+02 1.401224e+02 1.468980e+02
## 202 HI 5.108120e-01 0.103492693 4.391941e-01 5.165513e-01 5.820717e-01
## 203 HUE -1.565643e+00 0.002557956 -1.566518e+00 -1.565989e+00 -1.565329e+00
## 204 NGRDI 6.172331e-02 0.016111465 5.240332e-02 6.239724e-02 7.254210e-02
## 205 Red 1.271104e+02 16.608877875 1.180204e+02 1.239184e+02 1.309184e+02
## 206 SCI -6.172331e-02 0.016111465 -7.254210e-02 -6.239724e-02 -5.240332e-02
## 207 SI 2.575596e-01 0.049680653 2.412780e-01 2.635626e-01 2.833271e-01
## 208 VARI 8.516176e-02 0.021582952 7.164497e-02 8.562143e-02 1.001330e-01
## 209 BGI 5.693282e-01 0.056593919 5.373570e-01 5.590145e-01 5.809648e-01
## 210 BI 1.731334e+02 13.744272582 1.642061e+02 1.721689e+02 1.819137e+02
## 211 Blue 1.127348e+02 15.738512027 1.026531e+02 1.108571e+02 1.195102e+02
## 212 GLI 1.254280e-01 0.024619257 1.098302e-01 1.323107e-01 1.432109e-01
## 213 gray 1.871142e+02 13.543546750 1.786573e+02 1.865333e+02 1.959722e+02
## 214 Green 1.975219e+02 12.538211491 1.906939e+02 1.978163e+02 2.055102e+02
## 215 HI 9.367249e-01 0.243034728 7.793290e-01 8.354672e-01 1.043820e+00
## 216 HUE -7.191476e-01 1.382998132 -1.564824e+00 -1.562258e+00 1.539836e+00
## 217 NGRDI 7.404515e-03 0.025480404 -4.434350e-03 1.715658e-02 2.470712e-02
## 218 Red 1.950403e+02 17.959691647 1.831020e+02 1.922245e+02 2.059184e+02
## 219 SCI -7.404515e-03 0.025480404 -2.470712e-02 -1.715658e-02 4.434350e-03
## 220 SI 2.690076e-01 0.046694233 2.535684e-01 2.767424e-01 2.964936e-01
## 221 VARI 1.065781e-02 0.035268911 -6.111649e-03 2.440895e-02 3.453757e-02
## 222 BGI 5.249571e-01 0.062383919 4.972483e-01 5.128702e-01 5.325996e-01
## 223 BI 1.596943e+02 12.559718478 1.548423e+02 1.603918e+02 1.657405e+02
## 224 Blue 9.885602e+01 16.177372461 9.148980e+01 9.722449e+01 1.033878e+02
## 225 GLI 1.535078e-01 0.022791149 1.479623e-01 1.577169e-01 1.643334e-01
## 226 gray 1.744049e+02 12.635562207 1.696160e+02 1.755141e+02 1.809110e+02
## 227 Green 1.876461e+02 12.558593906 1.828163e+02 1.891735e+02 1.945102e+02
## 228 HI 7.633942e-01 0.086782133 7.026002e-01 7.546003e-01 8.001583e-01
## 229 HUE -1.502422e+00 0.425405434 -1.566482e+00 -1.565710e+00 -1.564183e+00
## 230 NGRDI 2.885060e-02 0.011169586 2.353950e-02 3.022025e-02 3.688901e-02
## 231 Red 1.772142e+02 13.124324413 1.724694e+02 1.781837e+02 1.842653e+02
## 232 SCI -2.885060e-02 0.011169586 -3.688901e-02 -3.022025e-02 -2.353950e-02
## 233 SI 2.873387e-01 0.046363354 2.775557e-01 2.957897e-01 3.104482e-01
## 234 VARI 3.941850e-02 0.014949344 3.248707e-02 4.090942e-02 5.043292e-02
## 235 BGI 5.450818e-01 0.071943333 5.070647e-01 5.309679e-01 5.598492e-01
## 236 BI 1.537413e+02 14.514226460 1.452709e+02 1.527578e+02 1.612965e+02
## 237 Blue 9.624747e+01 19.376548376 8.561224e+01 9.309184e+01 1.020816e+02
## 238 GLI 1.291434e-01 0.026067265 1.168399e-01 1.314071e-01 1.455899e-01
## 239 gray 1.663356e+02 14.218634914 1.581210e+02 1.656253e+02 1.743097e+02
## 240 Green 1.753847e+02 13.699549873 1.675102e+02 1.749388e+02 1.832041e+02
## 241 HI 9.989140e-01 0.154262450 9.007172e-01 9.970515e-01 1.107483e+00
## 242 HUE -1.918447e-02 1.533749487 -1.555708e+00 -1.059374e+00 1.556977e+00
## 243 NGRDI 6.146476e-04 0.017263368 -1.190220e-02 3.111587e-04 1.172595e-02
## 244 Red 1.752928e+02 15.070329664 1.658776e+02 1.751633e+02 1.850000e+02
## 245 SCI -6.146476e-04 0.017263368 -1.172595e-02 -3.111587e-04 1.190220e-02
## 246 SI 2.964377e-01 0.054446703 2.857464e-01 3.082798e-01 3.249796e-01
## 247 VARI 8.426975e-04 0.023651500 -1.632448e-02 4.306633e-04 1.596546e-02
## 248 BGI 5.233619e-01 0.067891340 4.894052e-01 5.096004e-01 5.332910e-01
## 249 BI 1.474502e+02 14.535469934 1.384085e+02 1.459328e+02 1.537968e+02
## 250 Blue 8.954300e+01 19.216559334 7.900000e+01 8.600000e+01 9.425000e+01
## 251 GLI 1.384899e-01 0.021684567 1.320047e-01 1.417562e-01 1.504612e-01
## 252 gray 1.601093e+02 14.665721191 1.507577e+02 1.587924e+02 1.671115e+02
## 253 Green 1.697510e+02 15.108230617 1.599592e+02 1.685306e+02 1.775510e+02
## 254 HI 9.600963e-01 0.117854449 8.875060e-01 9.615579e-01 1.041944e+00
## 255 HUE -3.763524e-01 1.476921598 -1.557815e+00 -1.530458e+00 1.532104e+00
## 256 NGRDI 4.506612e-03 0.013642025 -4.956473e-03 4.568824e-03 1.346833e-02
## 257 Red 1.680855e+02 13.224252460 1.596939e+02 1.674286e+02 1.750408e+02
## 258 SCI -4.506612e-03 0.013642025 -1.346833e-02 -4.568824e-03 4.956473e-03
## 259 SI 3.110701e-01 0.055923605 2.990188e-01 3.218473e-01 3.405766e-01
## 260 VARI 6.327836e-03 0.018573545 -6.720936e-03 6.251881e-03 1.827972e-02
## 261 BGI 5.046310e-01 0.074173138 4.675413e-01 4.885906e-01 5.146986e-01
## 262 BI 1.304131e+02 14.578500953 1.228730e+02 1.277702e+02 1.335876e+02
## 263 Blue 7.989120e+01 19.065019246 7.089796e+01 7.606122e+01 8.185714e+01
## 264 GLI 1.767202e-01 0.029184161 1.715935e-01 1.826426e-01 1.915274e-01
## 265 gray 1.434144e+02 14.204844472 1.357792e+02 1.410068e+02 1.472919e+02
## 266 Green 1.570034e+02 13.600259390 1.492755e+02 1.548980e+02 1.617347e+02
## 267 HI 5.833598e-01 0.059904895 5.446780e-01 5.815673e-01 6.262376e-01
## 268 HUE -1.566145e+00 0.001859905 -1.566971e+00 -1.566438e+00 -1.565851e+00
## 269 NGRDI 5.448390e-02 0.011367371 4.934291e-02 5.553927e-02 6.151109e-02
## 270 Red 1.409558e+02 14.516077053 1.334694e+02 1.384694e+02 1.446122e+02
## 271 SCI -5.448390e-02 0.011367371 -6.151109e-02 -5.553927e-02 -4.934291e-02
## 272 SI 2.830816e-01 0.052495161 2.686012e-01 2.924937e-01 3.122760e-01
## 273 VARI 7.379653e-02 0.014168969 6.635839e-02 7.524178e-02 8.276499e-02
## 274 BGI 5.484175e-01 0.070442358 5.122256e-01 5.371213e-01 5.601112e-01
## 275 BI 1.533915e+02 13.434078537 1.453086e+02 1.530967e+02 1.609798e+02
## 276 Blue 9.704841e+01 18.584819973 8.663776e+01 9.446939e+01 1.031224e+02
## 277 GLI 1.322866e-01 0.023462062 1.251843e-01 1.342537e-01 1.456666e-01
## 278 gray 1.661886e+02 13.226740967 1.581150e+02 1.663080e+02 1.742349e+02
## 279 Green 1.759391e+02 13.073849723 1.679439e+02 1.763061e+02 1.841429e+02
## 280 HI 9.349741e-01 0.109724640 8.743212e-01 9.328606e-01 9.960062e-01
## 281 HUE -8.118095e-01 1.290596417 -1.558512e+00 -1.547926e+00 -1.198032e+00
## 282 NGRDI 7.278826e-03 0.012102216 4.490535e-04 7.498980e-03 1.418686e-02
## 283 Red 1.734073e+02 12.994255287 1.656531e+02 1.738571e+02 1.819388e+02
## 284 SCI -7.278826e-03 0.012102216 -1.418686e-02 -7.498980e-03 -4.490535e-04
## 285 SI 2.873982e-01 0.053222932 2.765324e-01 2.968311e-01 3.157077e-01
## 286 VARI 1.009202e-02 0.016641445 6.187760e-04 1.041315e-02 1.960147e-02
## 287 BGI 5.207860e-01 0.071626850 4.865130e-01 5.107144e-01 5.329111e-01
## 288 BI 1.395525e+02 17.068653186 1.296181e+02 1.374920e+02 1.464138e+02
## 289 Blue 8.588464e+01 20.919796122 7.487245e+01 8.273469e+01 9.046939e+01
## 290 GLI 1.510602e-01 0.024898958 1.441184e-01 1.536548e-01 1.641193e-01
## 291 gray 1.521675e+02 17.227046719 1.419040e+02 1.503289e+02 1.599090e+02
## 292 Green 1.631570e+02 17.484097310 1.526888e+02 1.615204e+02 1.716531e+02
## 293 HI 8.131070e-01 0.109803057 7.391249e-01 8.103076e-01 8.779259e-01
## 294 HUE -1.437451e+00 0.585529535 -1.564366e+00 -1.561828e+00 -1.556131e+00
## 295 NGRDI 2.267746e-02 0.013105007 1.455317e-02 2.303205e-02 3.144623e-02
## 296 Red 1.558643e+02 16.289147783 1.463061e+02 1.538980e+02 1.632296e+02
## 297 SCI -2.267746e-02 0.013105007 -3.144623e-02 -2.303205e-02 -1.455317e-02
## 298 SI 2.972665e-01 0.056629525 2.825668e-01 3.029235e-01 3.255470e-01
## 299 VARI 3.093311e-02 0.017741945 1.989417e-02 3.141559e-02 4.288234e-02
## 300 BGI 5.862999e-01 0.059612981 5.543382e-01 5.791280e-01 6.022413e-01
## 301 BI 1.681839e+02 16.083602113 1.582333e+02 1.692416e+02 1.782315e+02
## 302 Blue 1.105194e+02 19.460263483 9.816327e+01 1.098163e+02 1.197857e+02
## 303 GLI 1.055336e-01 0.019042350 9.568728e-02 1.073600e-01 1.184691e-01
## 304 gray 1.804409e+02 16.688221981 1.701113e+02 1.819661e+02 1.913046e+02
## 305 Green 1.875037e+02 17.856387955 1.763776e+02 1.892041e+02 1.995510e+02
## 306 HI 1.155520e+00 0.186526044 1.016261e+00 1.171134e+00 1.281588e+00
## 307 HUE 8.590737e-01 1.280279638 1.479247e+00 1.562279e+00 1.565508e+00
## 308 NGRDI -1.585161e-02 0.018803599 -2.912053e-02 -1.746792e-02 -1.527957e-03
## 309 Red 1.932343e+02 14.976397222 1.844082e+02 1.944286e+02 2.035510e+02
## 310 SCI 1.585161e-02 0.018803599 1.527957e-03 1.746792e-02 2.912053e-02
## 311 SI 2.769417e-01 0.050696385 2.576928e-01 2.808089e-01 3.070842e-01
## 312 VARI -2.186420e-02 0.026067428 -4.019747e-02 -2.447727e-02 -2.192770e-03
## 313 BGI 5.192105e-01 0.077532642 4.832943e-01 5.051208e-01 5.279565e-01
## 314 BI 1.238033e+02 17.501235064 1.147951e+02 1.217751e+02 1.293339e+02
## 315 Blue 7.798505e+01 21.099634579 6.795408e+01 7.457143e+01 8.144898e+01
## 316 GLI 1.722109e-01 0.030406559 1.675139e-01 1.774346e-01 1.868156e-01
## 317 gray 1.358587e+02 17.297326137 1.266558e+02 1.341108e+02 1.421687e+02
## 318 Green 1.484667e+02 16.885049723 1.391020e+02 1.471429e+02 1.556990e+02
## 319 HI 5.641178e-01 0.083052966 5.081380e-01 5.643149e-01 6.267648e-01
## 320 HUE -1.565462e+00 0.002165983 -1.566662e+00 -1.565954e+00 -1.564926e+00
## 321 NGRDI 5.525277e-02 0.013887055 4.730644e-02 5.612322e-02 6.416064e-02
## 322 Red 1.331720e+02 17.650637197 1.240969e+02 1.314082e+02 1.392041e+02
## 323 SCI -5.525277e-02 0.013887055 -6.416064e-02 -5.612322e-02 -4.730644e-02
## 324 SI 2.693215e-01 0.053556320 2.545540e-01 2.765221e-01 2.984969e-01
## 325 VARI 7.563644e-02 0.018043312 6.424849e-02 7.658353e-02 8.744597e-02
## 326 BGI 5.297517e-01 0.089256861 4.888340e-01 5.099803e-01 5.341361e-01
## 327 BI 1.216734e+02 18.012412608 1.126482e+02 1.193315e+02 1.260506e+02
## 328 Blue 7.863188e+01 22.388229120 6.810204e+01 7.437755e+01 8.048469e+01
## 329 GLI 1.761516e-01 0.037100282 1.725122e-01 1.839830e-01 1.946178e-01
## 330 gray 1.335686e+02 17.555069909 1.245575e+02 1.317249e+02 1.387731e+02
## 331 Green 1.467441e+02 16.879598346 1.378980e+02 1.454898e+02 1.532041e+02
## 332 HI 4.695773e-01 0.078643417 4.138743e-01 4.563732e-01 5.141532e-01
## 333 HUE -1.563679e+00 0.075712046 -1.567170e+00 -1.566671e+00 -1.565895e+00
## 334 NGRDI 6.704229e-02 0.017179989 6.129031e-02 7.113344e-02 7.797700e-02
## 335 Red 1.286482e+02 18.256359011 1.196786e+02 1.265102e+02 1.332041e+02
## 336 SCI -6.704229e-02 0.017179989 -7.797700e-02 -7.113344e-02 -6.129031e-02
## 337 SI 2.500272e-01 0.055117424 2.396514e-01 2.602473e-01 2.776815e-01
## 338 VARI 9.240721e-02 0.021812318 8.464712e-02 9.768407e-02 1.070202e-01
## 339 BGI 5.237393e-01 0.070387743 4.916762e-01 5.131221e-01 5.342015e-01
## 340 BI 1.317087e+02 16.350242202 1.224276e+02 1.291465e+02 1.370149e+02
## 341 Blue 8.201910e+01 19.717060635 7.220408e+01 7.869388e+01 8.548980e+01
## 342 GLI 1.566632e-01 0.026455511 1.498524e-01 1.603016e-01 1.703397e-01
## 343 gray 1.438773e+02 16.275324712 1.343790e+02 1.413986e+02 1.496976e+02
## 344 Green 1.551395e+02 16.041928337 1.455102e+02 1.526122e+02 1.615102e+02
## 345 HI 7.323317e-01 0.119954555 6.488902e-01 7.256163e-01 8.124658e-01
## 346 HUE -1.509422e+00 0.385425590 -1.565185e+00 -1.563745e+00 -1.560412e+00
## 347 NGRDI 3.308341e-02 0.015572896 2.241998e-02 3.398126e-02 4.385281e-02
## 348 Red 1.453519e+02 16.578655801 1.355816e+02 1.432653e+02 1.517143e+02
## 349 SCI -3.308341e-02 0.015572896 -4.385281e-02 -3.398126e-02 -2.241998e-02
## 350 SI 2.850766e-01 0.052848437 2.707255e-01 2.926301e-01 3.129353e-01
## 351 VARI 4.523116e-02 0.021233134 3.039835e-02 4.646386e-02 5.972904e-02
## 352 BGI 5.220014e-01 0.069163883 4.854602e-01 5.079474e-01 5.379613e-01
## 353 BI 1.314475e+02 16.742216580 1.216378e+02 1.281832e+02 1.364430e+02
## 354 Blue 8.200062e+01 19.682137241 7.155612e+01 7.789796e+01 8.516327e+01
## 355 GLI 1.609071e-01 0.026412282 1.543688e-01 1.655955e-01 1.752237e-01
## 356 gray 1.437987e+02 16.683778915 1.337721e+02 1.407774e+02 1.494348e+02
## 357 Green 1.556073e+02 16.278291231 1.455969e+02 1.529592e+02 1.618571e+02
## 358 HI 6.893229e-01 0.094072275 6.356398e-01 6.773564e-01 7.316739e-01
## 359 HUE -1.530661e+00 0.314714694 -1.565630e+00 -1.564763e+00 -1.563580e+00
## 360 NGRDI 3.886822e-02 0.012793738 3.300436e-02 4.077430e-02 4.665043e-02
## 361 Red 1.441778e+02 17.289151362 1.337755e+02 1.407959e+02 1.500612e+02
## 362 SCI -3.886822e-02 0.012793738 -4.665043e-02 -4.077430e-02 -3.300436e-02
## 363 SI 2.812932e-01 0.050396682 2.641227e-01 2.898611e-01 3.107013e-01
## 364 VARI 5.307316e-02 0.017197156 4.510258e-02 5.548978e-02 6.320527e-02
## 365 BGI 5.238761e-01 0.064520931 4.887377e-01 5.123290e-01 5.402303e-01
## 366 BI 1.225945e+02 15.852749019 1.136842e+02 1.200678e+02 1.273741e+02
## 367 Blue 7.836941e+01 17.726274608 7.016327e+01 7.513265e+01 8.082143e+01
## 368 GLI 1.793160e-01 0.027298440 1.729080e-01 1.840040e-01 1.934194e-01
## 369 gray 1.348778e+02 15.820359143 1.255633e+02 1.324440e+02 1.403712e+02
## 370 Green 1.485640e+02 15.295992026 1.391020e+02 1.463265e+02 1.546735e+02
## 371 HI 4.534645e-01 0.109331974 3.731497e-01 4.491759e-01 5.323732e-01
## 372 HUE -1.566529e+00 0.001371604 -1.567189e+00 -1.566817e+00 -1.566281e+00
## 373 NGRDI 6.986914e-02 0.017007630 6.062804e-02 7.182358e-02 8.085661e-02
## 374 Red 1.295541e+02 17.225032577 1.194490e+02 1.268571e+02 1.360612e+02
## 375 SCI -6.986914e-02 0.017007630 -8.085661e-02 -7.182358e-02 -6.062804e-02
## 376 SI 2.504249e-01 0.045659934 2.293685e-01 2.541229e-01 2.796171e-01
## 377 VARI 9.673872e-02 0.023168280 8.277490e-02 9.924512e-02 1.125975e-01
## 378 BGI 5.354317e-01 0.071313996 4.986688e-01 5.225076e-01 5.487635e-01
## 379 BI 1.273232e+02 17.883226072 1.171775e+02 1.238624e+02 1.323961e+02
## 380 Blue 8.088840e+01 20.953698464 7.004082e+01 7.636735e+01 8.428571e+01
## 381 GLI 1.521554e-01 0.028233884 1.416277e-01 1.570313e-01 1.685301e-01
## 382 gray 1.388224e+02 17.808564274 1.285083e+02 1.355726e+02 1.444364e+02
## 383 Green 1.493427e+02 17.554683179 1.389184e+02 1.463061e+02 1.550816e+02
## 384 HI 7.364305e-01 0.132900042 6.589940e-01 7.132988e-01 8.114374e-01
## 385 HUE -1.382983e+00 0.708095175 -1.564830e+00 -1.563338e+00 -1.558951e+00
## 386 NGRDI 3.202228e-02 0.017438497 2.129780e-02 3.493512e-02 4.281298e-02
## 387 Red 1.402576e+02 18.095510463 1.294490e+02 1.371633e+02 1.471020e+02
## 388 SCI -3.202228e-02 0.017438497 -4.281298e-02 -3.493512e-02 -2.129780e-02
## 389 SI 2.759451e-01 0.051104317 2.621901e-01 2.830875e-01 3.049807e-01
## 390 VARI 4.400084e-02 0.023548247 2.953804e-02 4.806749e-02 5.846652e-02
## 391 BGI 4.965653e-01 0.079627745 4.576112e-01 4.790190e-01 5.062927e-01
## 392 BI 1.405538e+02 18.275841129 1.291275e+02 1.373098e+02 1.480468e+02
## 393 Blue 8.387305e+01 21.866658446 7.244898e+01 7.885714e+01 8.744898e+01
## 394 GLI 1.689130e-01 0.033347164 1.558015e-01 1.762004e-01 1.902043e-01
## 395 gray 1.540842e+02 18.102754957 1.427699e+02 1.510731e+02 1.620069e+02
## 396 Green 1.670471e+02 17.118456525 1.567092e+02 1.642449e+02 1.745918e+02
## 397 HI 7.176766e-01 0.156018401 6.036244e-01 6.936921e-01 8.038587e-01
## 398 HUE -1.386300e+00 0.715231681 -1.566762e+00 -1.565691e+00 -1.563144e+00
## 399 NGRDI 3.762537e-02 0.021819521 2.309526e-02 4.044680e-02 5.397942e-02
## 400 Red 1.554050e+02 20.171144561 1.412857e+02 1.523776e+02 1.666173e+02
## 401 SCI -3.762537e-02 0.021819521 -5.397942e-02 -4.044680e-02 -2.309526e-02
## 402 SI 3.063417e-01 0.059244516 2.909569e-01 3.187842e-01 3.387896e-01
## 403 VARI 5.033993e-02 0.028821026 3.174379e-02 5.402534e-02 7.145717e-02
## 404 BGI 5.211453e-01 0.069683216 4.899566e-01 5.078216e-01 5.281845e-01
## 405 BI 1.427829e+02 14.179526571 1.353625e+02 1.418720e+02 1.480226e+02
## 406 Blue 8.872163e+01 18.387599503 8.004082e+01 8.600000e+01 9.218367e+01
## 407 GLI 1.610264e-01 0.027185917 1.550044e-01 1.656042e-01 1.756278e-01
## 408 gray 1.562468e+02 13.884853956 1.487423e+02 1.557257e+02 1.620402e+02
## 409 Green 1.691127e+02 13.284698595 1.617092e+02 1.689490e+02 1.754082e+02
## 410 HI 6.927941e-01 0.097553236 6.341386e-01 6.812087e-01 7.398326e-01
## 411 HUE -1.533661e+00 0.302308024 -1.566560e+00 -1.565762e+00 -1.564461e+00
## 412 NGRDI 3.856387e-02 0.013762036 3.097191e-02 4.032369e-02 4.751631e-02
## 413 Red 1.567337e+02 14.609785540 1.484286e+02 1.560510e+02 1.632245e+02
## 414 SCI -3.856387e-02 0.013762036 -4.751631e-02 -4.032369e-02 -3.097191e-02
## 415 SI 2.823817e-01 0.049759910 2.722940e-01 2.911007e-01 3.073090e-01
## 416 VARI 5.256782e-02 0.018302823 4.258658e-02 5.482058e-02 6.426120e-02
## 417 BGI 5.554579e-01 0.070899774 5.164319e-01 5.414773e-01 5.696930e-01
## 418 BI 1.625161e+02 13.595106263 1.553504e+02 1.619489e+02 1.690269e+02
## 419 Blue 1.021844e+02 19.439602379 9.126531e+01 9.897959e+01 1.082653e+02
## 420 GLI 1.173093e-01 0.024944652 1.026677e-01 1.201435e-01 1.340897e-01
## 421 gray 1.750791e+02 13.771141478 1.678362e+02 1.749311e+02 1.823608e+02
## 422 Green 1.829619e+02 14.707116395 1.747143e+02 1.830612e+02 1.918163e+02
## 423 HI 1.115773e+00 0.221058421 9.355420e-01 1.069990e+00 1.256200e+00
## 424 HUE 4.073203e-01 1.486136146 -1.548492e+00 1.549868e+00 1.565141e+00
## 425 NGRDI -1.242718e-02 0.024077290 -2.765778e-02 -7.236320e-03 6.693440e-03
## 426 Red 1.873961e+02 12.679792582 1.814082e+02 1.877347e+02 1.941429e+02
## 427 SCI 1.242718e-02 0.024077290 -6.693440e-03 7.236320e-03 2.765778e-02
## 428 SI 2.993312e-01 0.060193384 2.797104e-01 3.094472e-01 3.342742e-01
## 429 VARI -1.665706e-02 0.032480951 -3.780662e-02 -1.023866e-02 9.349649e-03
## 430 BGI 5.400814e-01 0.071925536 5.025206e-01 5.282392e-01 5.562977e-01
## 431 BI 1.166009e+02 17.649485240 1.074675e+02 1.127090e+02 1.206427e+02
## 432 Blue 7.733526e+01 19.813312323 6.912245e+01 7.300000e+01 7.744898e+01
## 433 GLI 1.819086e-01 0.031842210 1.784304e-01 1.885623e-01 1.972451e-01
## 434 gray 1.281973e+02 17.401674800 1.187700e+02 1.244686e+02 1.332056e+02
## 435 Green 1.418990e+02 16.402334762 1.326735e+02 1.385306e+02 1.474490e+02
## 436 HI 3.358040e-01 0.141950025 2.295720e-01 3.342149e-01 4.326241e-01
## 437 HUE -1.566216e+00 0.034081696 -1.567239e+00 -1.566956e+00 -1.566581e+00
## 438 NGRDI 8.332864e-02 0.021703746 7.463147e-02 8.632743e-02 9.736409e-02
## 439 Red 1.206901e+02 19.444305373 1.095102e+02 1.164490e+02 1.271429e+02
## 440 SCI -8.332864e-02 0.021703746 -9.736409e-02 -8.632743e-02 -7.463147e-02
## 441 SI 2.237954e-01 0.049262601 1.963739e-01 2.257212e-01 2.552688e-01
## 442 VARI 1.172587e-01 0.030378973 1.027964e-01 1.203482e-01 1.378702e-01
## 443 BGI 5.346590e-01 0.072480016 4.939433e-01 5.217933e-01 5.500966e-01
## 444 BI 1.586044e+02 15.232760193 1.480257e+02 1.592621e+02 1.674833e+02
## 445 Blue 9.974399e+01 19.625582486 8.767347e+01 9.753061e+01 1.065306e+02
## 446 GLI 1.492086e-01 0.030265053 1.345262e-01 1.519202e-01 1.694415e-01
## 447 gray 1.728196e+02 14.910294112 1.625260e+02 1.740088e+02 1.820270e+02
## 448 Green 1.853867e+02 13.983644221 1.761429e+02 1.864490e+02 1.939796e+02
## 449 HI 7.831766e-01 0.157492115 6.635638e-01 7.496516e-01 8.944681e-01
## 450 HUE -1.195512e+00 0.992478235 -1.566905e+00 -1.565174e+00 -1.557669e+00
## 451 NGRDI 2.685379e-02 0.020567838 1.169958e-02 2.797829e-02 4.233799e-02
## 452 Red 1.760092e+02 16.887354429 1.637551e+02 1.780816e+02 1.872653e+02
## 453 SCI -2.685379e-02 0.020567838 -4.233799e-02 -2.797829e-02 -1.169958e-02
## 454 SI 2.815520e-01 0.052593393 2.700558e-01 2.900691e-01 3.096276e-01
## 455 VARI 3.671980e-02 0.027712050 1.633129e-02 3.888313e-02 5.778265e-02
## 456 BGI 5.329802e-01 0.068257145 4.972797e-01 5.179919e-01 5.447359e-01
## 457 BI 1.635488e+02 12.945816619 1.563341e+02 1.626355e+02 1.691450e+02
## 458 Blue 1.015314e+02 18.453136243 9.114286e+01 9.816327e+01 1.066735e+02
## 459 GLI 1.421414e-01 0.024387848 1.354700e-01 1.466366e-01 1.556862e-01
## 460 gray 1.778464e+02 12.679172879 1.707905e+02 1.773190e+02 1.839119e+02
## 461 Green 1.895340e+02 12.360020850 1.827551e+02 1.893469e+02 1.959796e+02
## 462 HI 8.747204e-01 0.131212043 7.900273e-01 8.588413e-01 9.270067e-01
## 463 HUE -1.168346e+00 1.016582098 -1.564745e+00 -1.561492e+00 -1.552287e+00
## 464 NGRDI 1.498786e-02 0.014486335 8.352107e-03 1.641707e-02 2.505604e-02
## 465 Red 1.839979e+02 12.930806008 1.768367e+02 1.834286e+02 1.907908e+02
## 466 SCI -1.498786e-02 0.014486335 -2.505604e-02 -1.641707e-02 -8.352107e-03
## 467 SI 2.934039e-01 0.052761594 2.782379e-01 3.035117e-01 3.241928e-01
## 468 VARI 2.054019e-02 0.019810177 1.169519e-02 2.246015e-02 3.427607e-02
## 469 BGI 4.952852e-01 0.074527453 4.633113e-01 4.770828e-01 4.955592e-01
## 470 BI 1.483821e+02 13.641513684 1.411250e+02 1.470247e+02 1.536048e+02
## 471 Blue 8.830111e+01 19.299668071 7.940816e+01 8.428571e+01 9.048469e+01
## 472 GLI 1.708486e-01 0.028348125 1.664811e-01 1.775059e-01 1.849260e-01
## 473 gray 1.629022e+02 13.177974186 1.557700e+02 1.620764e+02 1.687101e+02
## 474 Green 1.770550e+02 12.436010126 1.703316e+02 1.767755e+02 1.831429e+02
## 475 HI 6.954710e-01 0.075476999 6.471875e-01 6.810771e-01 7.264981e-01
## 476 HUE -1.554096e+00 0.184547448 -1.567000e+00 -1.566607e+00 -1.565683e+00
## 477 NGRDI 4.007713e-02 0.011720850 3.398657e-02 4.252720e-02 4.800629e-02
## 478 Red 1.635605e+02 13.610611750 1.558367e+02 1.626531e+02 1.703469e+02
## 479 SCI -4.007713e-02 0.011720850 -4.800629e-02 -4.252720e-02 -3.398657e-02
## 480 SI 3.047971e-01 0.054012704 3.008835e-01 3.168290e-01 3.300526e-01
## 481 VARI 5.365908e-02 0.014958927 4.605950e-02 5.680146e-02 6.384184e-02
## 482 BGI 5.442703e-01 0.061071971 5.108391e-01 5.316004e-01 5.582811e-01
## 483 BI 1.716234e+02 14.241336350 1.631903e+02 1.709974e+02 1.790346e+02
## 484 Blue 1.089607e+02 17.636281895 9.822449e+01 1.065102e+02 1.161020e+02
## 485 GLI 1.421834e-01 0.021910846 1.347103e-01 1.470360e-01 1.555523e-01
## 486 gray 1.866801e+02 14.352778874 1.783008e+02 1.863492e+02 1.944195e+02
## 487 Green 1.994104e+02 14.152619315 1.912653e+02 1.997143e+02 2.073878e+02
## 488 HI 8.212868e-01 0.070612097 7.768051e-01 8.064516e-01 8.744676e-01
## 489 HUE -1.538299e+00 0.264743541 -1.565584e+00 -1.564448e+00 -1.561154e+00
## 490 NGRDI 2.093508e-02 0.008721118 1.436158e-02 2.239640e-02 2.677305e-02
## 491 Red 1.913201e+02 14.850767867 1.826939e+02 1.904796e+02 1.994898e+02
## 492 SCI -2.093508e-02 0.008721118 -2.677305e-02 -2.239640e-02 -1.436158e-02
## 493 SI 2.778811e-01 0.044888872 2.657416e-01 2.845454e-01 3.020852e-01
## 494 VARI 2.884547e-02 0.011799911 1.994195e-02 3.088715e-02 3.656663e-02
## 495 BGI 5.609856e-01 0.083853109 5.083240e-01 5.498266e-01 5.932783e-01
## 496 BI 1.670620e+02 17.653470060 1.547940e+02 1.676717e+02 1.777593e+02
## 497 Blue 1.075657e+02 23.939310334 9.142857e+01 1.054082e+02 1.186122e+02
## 498 GLI 1.253213e-01 0.029575411 1.108515e-01 1.287683e-01 1.441070e-01
## 499 gray 1.804022e+02 17.343059987 1.685135e+02 1.814482e+02 1.917514e+02
## 500 Green 1.900296e+02 16.765897302 1.789388e+02 1.913469e+02 2.014286e+02
## 501 HI 9.881746e-01 0.136377215 8.972003e-01 9.842738e-01 1.071831e+00
## 502 HUE -1.310478e-01 1.526569539 -1.558046e+00 -1.485676e+00 1.551483e+00
## 503 NGRDI 2.175897e-03 0.014219689 -7.472696e-03 1.663536e-03 1.156175e-02
## 504 Red 1.892721e+02 17.445625372 1.773469e+02 1.901020e+02 2.008571e+02
## 505 SCI -2.175897e-03 0.014219689 -1.156175e-02 -1.663536e-03 7.472696e-03
## 506 SI 2.828440e-01 0.063074857 2.559740e-01 2.878431e-01 3.218741e-01
## 507 VARI 2.831771e-03 0.019719296 -1.061048e-02 2.336085e-03 1.601880e-02
## 508 BGI 4.907328e-01 0.082737798 4.463164e-01 4.736623e-01 5.029475e-01
## 509 BI 1.258034e+02 17.748456556 1.152794e+02 1.224017e+02 1.316392e+02
## 510 Blue 7.546188e+01 21.803555833 6.334694e+01 7.081633e+01 7.971429e+01
## 511 GLI 1.814295e-01 0.035422105 1.730819e-01 1.888755e-01 2.022825e-01
## 512 gray 1.384293e+02 17.366016781 1.278518e+02 1.353896e+02 1.449447e+02
## 513 Green 1.516203e+02 16.236901794 1.413265e+02 1.493469e+02 1.586735e+02
## 514 HI 6.031791e-01 0.133998693 5.242137e-01 5.767701e-01 6.542321e-01
## 515 HUE -1.513842e+00 0.393318934 -1.566849e+00 -1.566225e+00 -1.565233e+00
## 516 NGRDI 5.401157e-02 0.020239338 4.432275e-02 5.768847e-02 6.762455e-02
## 517 Red 1.365403e+02 18.948153899 1.247347e+02 1.330000e+02 1.434286e+02
## 518 SCI -5.401157e-02 0.020239338 -6.762455e-02 -5.768847e-02 -4.432275e-02
## 519 SI 2.973053e-01 0.059220510 2.826931e-01 3.059369e-01 3.296189e-01
## 520 VARI 7.225054e-02 0.026248824 5.963046e-02 7.719531e-02 8.968269e-02
## 521 BGI 5.651627e-01 0.070270315 5.274869e-01 5.538134e-01 5.826988e-01
## 522 BI 1.670819e+02 15.641189025 1.580787e+02 1.673641e+02 1.760445e+02
## 523 Blue 1.076521e+02 20.462167790 9.581633e+01 1.058367e+02 1.157143e+02
## 524 GLI 1.207909e-01 0.022726178 1.122594e-01 1.245610e-01 1.342208e-01
## 525 gray 1.802434e+02 15.751071332 1.714407e+02 1.807724e+02 1.899738e+02
## 526 Green 1.893013e+02 16.011764860 1.803878e+02 1.900204e+02 1.997755e+02
## 527 HI 1.022842e+00 0.144981807 9.306627e-01 1.003411e+00 1.127672e+00
## 528 HUE 6.781276e-02 1.515971846 -1.552779e+00 1.209506e+00 1.559581e+00
## 529 NGRDI -2.392550e-03 0.015107182 -1.358419e-02 -3.861856e-04 8.088395e-03
## 530 Red 1.901378e+02 15.217866158 1.816122e+02 1.912245e+02 1.996122e+02
## 531 SCI 2.392550e-03 0.015107182 -8.088395e-03 3.861856e-04 1.358419e-02
## 532 SI 2.823076e-01 0.055760004 2.632192e-01 2.889896e-01 3.141424e-01
## 533 VARI -3.142441e-03 0.020961349 -1.872552e-02 -5.305826e-04 1.108096e-02
## 534 BGI 5.439335e-01 0.075919415 5.029933e-01 5.276799e-01 5.597873e-01
## 535 BI 1.440937e+02 17.319382774 1.331346e+02 1.421219e+02 1.508383e+02
## 536 Blue 9.128441e+01 22.083875611 7.859184e+01 8.683673e+01 9.681633e+01
## 537 GLI 1.369913e-01 0.024998268 1.291644e-01 1.409693e-01 1.518008e-01
## 538 gray 1.562958e+02 17.194593334 1.453090e+02 1.546045e+02 1.635488e+02
## 539 Green 1.660256e+02 17.041478112 1.550612e+02 1.645306e+02 1.734898e+02
## 540 HI 8.913054e-01 0.108882529 8.199500e-01 8.882682e-01 9.541194e-01
## 541 HUE -1.120553e+00 1.048817281 -1.561080e+00 -1.555048e+00 -1.532264e+00
## 542 NGRDI 1.233694e-02 0.011861531 5.203341e-03 1.300793e-02 2.096676e-02
## 543 Red 1.619811e+02 16.601483266 1.513265e+02 1.609592e+02 1.696735e+02
## 544 SCI -1.233694e-02 0.011861531 -2.096676e-02 -1.300793e-02 -5.203341e-03
## 545 SI 2.869752e-01 0.058659716 2.703882e-01 2.991266e-01 3.199344e-01
## 546 VARI 1.708056e-02 0.016435109 7.163965e-03 1.779963e-02 2.903145e-02
## 547 BGI 5.159173e-01 0.063766630 4.829690e-01 5.044009e-01 5.278829e-01
## 548 BI 1.576645e+02 13.721336042 1.495583e+02 1.582037e+02 1.651035e+02
## 549 Blue 9.650251e+01 17.179029550 8.663265e+01 9.479592e+01 1.023673e+02
## 550 GLI 1.588122e-01 0.023509295 1.527699e-01 1.627243e-01 1.710447e-01
## 551 gray 1.724887e+02 13.820421603 1.643430e+02 1.733682e+02 1.805926e+02
## 552 Green 1.861704e+02 13.624547658 1.781429e+02 1.873673e+02 1.948367e+02
## 553 HI 7.408370e-01 0.050689452 7.052632e-01 7.397849e-01 7.737494e-01
## 554 HUE -1.564936e+00 0.036986414 -1.566522e+00 -1.565863e+00 -1.565061e+00
## 555 NGRDI 3.237852e-02 0.007694591 2.758137e-02 3.252652e-02 3.811958e-02
## 556 Red 1.745999e+02 14.179625477 1.664898e+02 1.757143e+02 1.826327e+02
## 557 SCI -3.237852e-02 0.007694591 -3.811958e-02 -3.252652e-02 -2.758137e-02
## 558 SI 2.922959e-01 0.047084031 2.800412e-01 2.996543e-01 3.180760e-01
## 559 VARI 4.392920e-02 0.009710020 3.760518e-02 4.396396e-02 5.151563e-02
## 560 BGI 5.666081e-01 0.066185437 5.313483e-01 5.560075e-01 5.815648e-01
## 561 BI 1.672388e+02 15.254461292 1.578224e+02 1.671204e+02 1.766321e+02
## 562 Blue 1.078770e+02 19.747760866 9.587755e+01 1.055918e+02 1.165510e+02
## 563 GLI 1.197491e-01 0.022304631 1.106805e-01 1.219795e-01 1.330704e-01
## 564 gray 1.803647e+02 15.424924103 1.707613e+02 1.805379e+02 1.904228e+02
## 565 Green 1.893122e+02 15.887831016 1.791429e+02 1.895816e+02 2.003673e+02
## 566 HI 1.030893e+00 0.167196006 9.074960e-01 1.016514e+00 1.163739e+00
## 567 HUE 8.921013e-02 1.530465766 -1.556655e+00 1.472025e+00 1.562077e+00
## 568 NGRDI -3.208784e-03 0.017743870 -1.747636e-02 -1.633745e-03 1.018313e-02
## 569 Red 1.904362e+02 14.821982349 1.813061e+02 1.910204e+02 2.002449e+02
## 570 SCI 3.208784e-03 0.017743870 -1.018313e-02 1.633745e-03 1.747636e-02
## 571 SI 2.815712e-01 0.053005356 2.629915e-01 2.887514e-01 3.138900e-01
## 572 VARI -4.231938e-03 0.024516247 -2.408287e-02 -2.268541e-03 1.417627e-02
## 573 BGI 5.055943e-01 0.073446911 4.700520e-01 4.922193e-01 5.176691e-01
## 574 BI 1.281643e+02 18.853688053 1.168745e+02 1.254604e+02 1.346853e+02
## 575 Blue 7.861401e+01 21.139395268 6.780102e+01 7.522449e+01 8.159184e+01
## 576 GLI 1.740048e-01 0.030266609 1.668375e-01 1.782952e-01 1.895035e-01
## 577 gray 1.407645e+02 18.996462889 1.290265e+02 1.382042e+02 1.481471e+02
## 578 Green 1.536511e+02 18.707555429 1.417959e+02 1.510612e+02 1.619949e+02
## 579 HI 6.128575e-01 0.103030323 5.365854e-01 6.287918e-01 6.766391e-01
## 580 HUE -1.565167e+00 0.003559577 -1.566714e+00 -1.566028e+00 -1.564698e+00
## 581 NGRDI 5.071917e-02 0.016691568 4.042076e-02 4.989788e-02 6.258580e-02
## 582 Red 1.391616e+02 19.824444129 1.269439e+02 1.366020e+02 1.471990e+02
## 583 SCI -5.071917e-02 0.016691568 -6.258580e-02 -4.989788e-02 -4.042076e-02
## 584 SI 2.856032e-01 0.052925437 2.693275e-01 2.942268e-01 3.141070e-01
## 585 VARI 6.865797e-02 0.021810797 5.478477e-02 6.691776e-02 8.472075e-02
## 586 BGI 5.695572e-01 0.063098581 5.349127e-01 5.587229e-01 5.829474e-01
## 587 BI 1.717000e+02 13.650988803 1.640337e+02 1.711276e+02 1.786286e+02
## 588 Blue 1.124495e+02 18.462811866 1.020408e+02 1.097755e+02 1.186327e+02
## 589 GLI 1.277223e-01 0.022444609 1.191524e-01 1.299448e-01 1.420012e-01
## 590 gray 1.857400e+02 13.433976159 1.782143e+02 1.855378e+02 1.930289e+02
## 591 Green 1.965788e+02 13.214038616 1.892653e+02 1.969796e+02 2.040000e+02
## 592 HI 9.041533e-01 0.101551396 8.174045e-01 8.848758e-01 9.536239e-01
## 593 HUE -1.041056e+00 1.127944155 -1.563560e+00 -1.559081e+00 -1.540524e+00
## 594 NGRDI 1.080787e-02 0.011320207 4.620462e-03 1.198167e-02 1.990020e-02
## 595 Red 1.924048e+02 13.362369902 1.847041e+02 1.923673e+02 2.003265e+02
## 596 SCI -1.080787e-02 0.011320207 -1.990020e-02 -1.198167e-02 -4.620462e-03
## 597 SI 2.662022e-01 0.045228656 2.551637e-01 2.741124e-01 2.908000e-01
## 598 VARI 1.505163e-02 0.015658227 6.570312e-03 1.693834e-02 2.800701e-02
## 599 BGI 5.136999e-01 0.067818433 4.833272e-01 5.003712e-01 5.217761e-01
## 600 BI 1.198540e+02 15.102103461 1.123906e+02 1.172470e+02 1.230915e+02
## 601 Blue 7.425426e+01 18.716515505 6.595918e+01 7.106122e+01 7.637755e+01
## 602 GLI 1.685783e-01 0.030022294 1.558108e-01 1.732643e-01 1.882053e-01
## 603 gray 1.314305e+02 14.821883632 1.239274e+02 1.291451e+02 1.352574e+02
## 604 Green 1.430537e+02 14.567105097 1.352653e+02 1.414286e+02 1.478163e+02
## 605 HI 6.382261e-01 0.176387068 4.856875e-01 6.291667e-01 7.384014e-01
## 606 HUE -1.461387e+00 0.531711148 -1.566605e+00 -1.564688e+00 -1.561563e+00
## 607 NGRDI 4.680895e-02 0.024643389 3.050796e-02 4.601382e-02 6.880573e-02
## 608 Red 1.304114e+02 15.213986066 1.219286e+02 1.274082e+02 1.345918e+02
## 609 SCI -4.680895e-02 0.024643389 -6.880573e-02 -4.601382e-02 -3.050796e-02
## 610 SI 2.811871e-01 0.050980546 2.636429e-01 2.847900e-01 3.105250e-01
## 611 VARI 6.386241e-02 0.033348409 4.215504e-02 6.274008e-02 9.372058e-02
## 612 BGI 5.233699e-01 0.071366342 4.852351e-01 5.082110e-01 5.365184e-01
## 613 BI 1.540889e+02 17.517171455 1.424442e+02 1.531945e+02 1.640565e+02
## 614 Blue 9.335171e+01 21.686472899 8.009184e+01 8.948980e+01 1.016122e+02
## 615 GLI 1.357483e-01 0.025168397 1.239059e-01 1.393655e-01 1.524221e-01
## 616 gray 1.671046e+02 18.089294852 1.549565e+02 1.665128e+02 1.779650e+02
## 617 Green 1.767388e+02 19.283769323 1.636531e+02 1.761224e+02 1.888571e+02
## 618 HI 9.959410e-01 0.203502594 8.178332e-01 9.719725e-01 1.165941e+00
## 619 HUE -1.038766e-01 1.543646333 -1.563215e+00 -1.517131e+00 1.561744e+00
## 620 NGRDI 4.205196e-04 0.024052663 -1.981832e-02 3.196535e-03 2.071091e-02
## 621 Red 1.763105e+02 16.493211830 1.652245e+02 1.762245e+02 1.869388e+02
## 622 SCI -4.205196e-04 0.024052663 -2.071091e-02 -3.196535e-03 1.981832e-02
## 623 SI 3.148103e-01 0.062226054 2.904577e-01 3.268891e-01 3.535252e-01
## 624 VARI 1.060480e-03 0.032360432 -2.630472e-02 4.421577e-03 2.886495e-02
## 625 BGI 5.090994e-01 0.074640085 4.719389e-01 4.961193e-01 5.220881e-01
## 626 BI 1.261842e+02 19.187278450 1.141480e+02 1.223852e+02 1.319595e+02
## 627 Blue 7.658158e+01 22.064905577 6.455102e+01 7.152041e+01 7.990306e+01
## 628 GLI 1.579552e-01 0.029446425 1.450048e-01 1.615677e-01 1.764518e-01
## 629 gray 1.378492e+02 19.436966167 1.252792e+02 1.343742e+02 1.445752e+02
## 630 Green 1.483860e+02 19.731447740 1.351633e+02 1.453571e+02 1.560051e+02
## 631 HI 7.845365e-01 0.159833062 6.716482e-01 7.406094e-01 8.911468e-01
## 632 HUE -1.161258e+00 1.021305644 -1.565094e+00 -1.562742e+00 -1.550539e+00
## 633 NGRDI 2.734472e-02 0.021018167 1.268927e-02 3.069510e-02 4.376964e-02
## 634 Red 1.405229e+02 19.127103160 1.281837e+02 1.366531e+02 1.476939e+02
## 635 SCI -2.734472e-02 0.021018167 -4.376964e-02 -3.069510e-02 -1.268927e-02
## 636 SI 3.036723e-01 0.058445554 2.857008e-01 3.136611e-01 3.372737e-01
## 637 VARI 3.699704e-02 0.028058685 1.751993e-02 4.203108e-02 5.874688e-02
## 638 BGI 6.078474e-01 0.053070687 5.768072e-01 5.972071e-01 6.251866e-01
## 639 BI 1.867474e+02 13.368470137 1.806434e+02 1.866594e+02 1.943435e+02
## 640 Blue 1.286178e+02 16.006173385 1.193878e+02 1.264694e+02 1.362449e+02
## 641 GLI 1.133765e-01 0.018648996 1.052220e-01 1.163055e-01 1.247095e-01
## 642 gray 2.009108e+02 13.510402642 1.951436e+02 2.013281e+02 2.087615e+02
## 643 Green 2.112135e+02 13.470802690 2.058980e+02 2.122245e+02 2.191633e+02
## 644 HI 9.306279e-01 0.094401483 8.666211e-01 9.325843e-01 9.744526e-01
## 645 HUE -9.545181e-01 1.190772573 -1.561704e+00 -1.553566e+00 -1.518738e+00
## 646 NGRDI 7.204478e-03 0.009141375 2.319307e-03 7.101086e-03 1.381579e-02
## 647 Red 2.082477e+02 14.195514712 2.020408e+02 2.085918e+02 2.166327e+02
## 648 SCI -7.204478e-03 0.009141375 -1.381579e-02 -7.101086e-03 -2.319307e-03
## 649 SI 2.384427e-01 0.036727602 2.269988e-01 2.445077e-01 2.597786e-01
## 650 VARI 1.027617e-02 0.013126463 3.409407e-03 1.006711e-02 1.984564e-02
## 651 BGI 4.952912e-01 0.083176449 4.551537e-01 4.763584e-01 4.980520e-01
## 652 BI 1.370597e+02 16.845397255 1.276754e+02 1.353550e+02 1.432755e+02
## 653 Blue 8.254839e+01 21.922877265 7.188265e+01 7.875510e+01 8.534694e+01
## 654 GLI 1.783104e-01 0.032992951 1.746986e-01 1.845826e-01 1.950874e-01
## 655 gray 1.507312e+02 16.440247545 1.413634e+02 1.494971e+02 1.576889e+02
## 656 Green 1.648639e+02 15.608272220 1.557755e+02 1.642653e+02 1.721786e+02
## 657 HI 6.140678e-01 0.064753533 5.465704e-01 6.327898e-01 6.645768e-01
## 658 HUE -1.566132e+00 0.010084477 -1.567193e+00 -1.566778e+00 -1.566183e+00
## 659 NGRDI 5.154886e-02 0.013234603 4.475784e-02 5.044159e-02 6.250312e-02
## 660 Red 1.489818e+02 17.125448397 1.387959e+02 1.476327e+02 1.567755e+02
## 661 SCI -5.154886e-02 0.013234603 -6.250312e-02 -5.044159e-02 -4.475784e-02
## 662 SI 2.952387e-01 0.057882639 2.873269e-01 3.077318e-01 3.252201e-01
## 663 VARI 6.910328e-02 0.016349416 5.998907e-02 6.696758e-02 8.424664e-02
## 664 BGI 5.336188e-01 0.069163995 4.974636e-01 5.207194e-01 5.435871e-01
## 665 BI 1.628448e+02 14.040329393 1.551261e+02 1.632000e+02 1.703755e+02
## 666 Blue 1.019810e+02 19.183728951 9.108163e+01 9.983673e+01 1.084082e+02
## 667 GLI 1.473044e-01 0.022631135 1.439975e-01 1.510857e-01 1.584462e-01
## 668 gray 1.774100e+02 14.015288664 1.696882e+02 1.782003e+02 1.856003e+02
## 669 Green 1.900616e+02 14.075069732 1.823061e+02 1.911429e+02 1.987959e+02
## 670 HI 7.994245e-01 0.068526871 7.397882e-01 8.057697e-01 8.494624e-01
## 671 HUE -1.548913e+00 0.198724403 -1.565273e+00 -1.564533e+00 -1.562722e+00
## 672 NGRDI 2.346648e-02 0.007541160 1.899536e-02 2.353198e-02 2.749106e-02
## 673 Red 1.813310e+02 13.270621244 1.743878e+02 1.821837e+02 1.890816e+02
## 674 SCI -2.346648e-02 0.007541160 -2.749106e-02 -2.353198e-02 -1.899536e-02
## 675 SI 2.851300e-01 0.052935122 2.714772e-01 2.935806e-01 3.165445e-01
## 676 VARI 3.228889e-02 0.010389648 2.570829e-02 3.227931e-02 3.721519e-02
## 677 BGI 5.363549e-01 0.066370741 4.990220e-01 5.208711e-01 5.507217e-01
## 678 BI 1.622543e+02 13.517443985 1.535825e+02 1.610629e+02 1.692846e+02
## 679 Blue 1.016159e+02 17.771590643 9.083673e+01 9.851020e+01 1.077755e+02
## 680 GLI 1.441727e-01 0.026540456 1.330787e-01 1.498287e-01 1.615670e-01
## 681 gray 1.765660e+02 13.151570506 1.683397e+02 1.758571e+02 1.838684e+02
## 682 Green 1.886103e+02 12.235751556 1.814082e+02 1.885510e+02 1.957347e+02
## 683 HI 8.399318e-01 0.128388623 7.240705e-01 8.250756e-01 9.142663e-01
## 684 HUE -1.225113e+00 0.941316253 -1.565847e+00 -1.563267e+00 -1.555597e+00
## 685 NGRDI 1.982248e-02 0.015405983 9.541884e-03 2.033617e-02 3.306392e-02
## 686 Red 1.814967e+02 14.877843221 1.715918e+02 1.800000e+02 1.905918e+02
## 687 SCI -1.982248e-02 0.015405983 -3.306392e-02 -2.033617e-02 -9.541884e-03
## 688 SI 2.860697e-01 0.046315678 2.752465e-01 2.956003e-01 3.116864e-01
## 689 VARI 2.697639e-02 0.020994841 1.335392e-02 2.796167e-02 4.544025e-02
## 690 BGI 6.234243e-01 0.051347026 5.922737e-01 6.224598e-01 6.486731e-01
## 691 BI 1.843406e+02 12.407116365 1.776757e+02 1.858557e+02 1.925398e+02
## 692 Blue 1.293257e+02 16.050989182 1.193776e+02 1.301837e+02 1.389592e+02
## 693 GLI 1.058335e-01 0.016277606 9.732254e-02 1.061799e-01 1.163689e-01
## 694 gray 1.976974e+02 12.384748219 1.912227e+02 1.993255e+02 2.059830e+02
## 695 Green 2.068853e+02 12.362877362 2.006224e+02 2.085510e+02 2.151020e+02
## 696 HI 9.713776e-01 0.070392318 9.328708e-01 9.742944e-01 1.000000e+00
## 697 HUE -6.490884e-01 1.295622142 -1.550777e+00 -1.517756e+00 0.000000e+00
## 698 NGRDI 2.770496e-03 0.006281432 0.000000e+00 2.355203e-03 6.779014e-03
## 699 Red 2.057279e+02 12.046072879 1.995306e+02 2.077347e+02 2.138776e+02
## 700 SCI -2.770496e-03 0.006281432 -6.779014e-03 -2.355203e-03 0.000000e+00
## 701 SI 2.305398e-01 0.037646495 2.102963e-01 2.289809e-01 2.537458e-01
## 702 VARI 4.026234e-03 0.009183291 0.000000e+00 3.453866e-03 9.674783e-03
## 703 BGI 5.906602e-01 0.061360229 5.524775e-01 5.819846e-01 6.144118e-01
## 704 BI 1.843845e+02 13.148442961 1.763827e+02 1.839973e+02 1.917711e+02
## 705 Blue 1.211344e+02 17.486057261 1.104796e+02 1.186327e+02 1.285510e+02
## 706 GLI 1.017174e-01 0.018319622 9.203013e-02 1.035036e-01 1.132234e-01
## 707 gray 1.975377e+02 13.297464069 1.894378e+02 1.975564e+02 2.055002e+02
## 708 Green 2.045594e+02 13.395182083 1.964082e+02 2.049592e+02 2.127755e+02
## 709 HI 1.200024e+00 0.124504484 1.132856e+00 1.201386e+00 1.278084e+00
## 710 HUE 1.345772e+00 0.781644949 1.561811e+00 1.564769e+00 1.566465e+00
## 711 NGRDI -1.996159e-02 0.012247129 -2.809257e-02 -2.050877e-02 -1.378561e-02
## 712 Red 2.128829e+02 13.755183913 2.042653e+02 2.130612e+02 2.218980e+02
## 713 SCI 1.996159e-02 0.012247129 1.378561e-02 2.050877e-02 2.809257e-02
## 714 SI 2.775080e-01 0.049154190 2.554118e-01 2.839601e-01 3.094629e-01
## 715 VARI -2.788813e-02 0.017001666 -3.912620e-02 -2.866801e-02 -1.930492e-02
## 716 BGI 4.767877e-01 0.076903317 4.424008e-01 4.592376e-01 4.794548e-01
## 717 BI 1.392395e+02 15.366369679 1.317755e+02 1.375969e+02 1.444194e+02
## 718 Blue 8.129589e+01 20.211299872 7.193878e+01 7.738776e+01 8.410204e+01
## 719 GLI 1.872960e-01 0.030701071 1.849219e-01 1.949402e-01 2.019633e-01
## 720 gray 1.536365e+02 15.083155810 1.461844e+02 1.525704e+02 1.595581e+02
## 721 Green 1.688923e+02 14.408747167 1.617755e+02 1.686531e+02 1.754694e+02
## 722 HI 5.981487e-01 0.046545932 5.505618e-01 6.099713e-01 6.369103e-01
## 723 HUE -1.566952e+00 0.001345488 -1.567621e+00 -1.567265e+00 -1.566838e+00
## 724 NGRDI 5.572581e-02 0.011047152 5.159066e-02 5.675984e-02 6.399175e-02
## 725 Red 1.512674e+02 15.483874450 1.435153e+02 1.495510e+02 1.568367e+02
## 726 SCI -5.572581e-02 0.011047152 -6.399175e-02 -5.675984e-02 -5.159066e-02
## 727 SI 3.083466e-01 0.055223945 3.004248e-01 3.178120e-01 3.355263e-01
## 728 VARI 7.396256e-02 0.013183607 6.808397e-02 7.355888e-02 8.496368e-02
## 729 BGI 6.032492e-01 0.062880036 5.665221e-01 5.984609e-01 6.301392e-01
## 730 BI 1.736074e+02 28.457258013 1.659649e+02 1.813097e+02 1.920285e+02
## 731 Blue 1.163127e+02 26.091302186 1.051224e+02 1.199388e+02 1.320816e+02
## 732 GLI 9.616794e-02 0.045626857 8.176099e-02 1.083292e-01 1.254361e-01
## 733 gray 1.857804e+02 31.707609235 1.792353e+02 1.953846e+02 2.059950e+02
## 734 Green 1.927309e+02 36.476874261 1.885918e+02 2.056531e+02 2.150816e+02
## 735 HI 1.295403e+00 0.725287264 9.073698e-01 1.012385e+00 1.266070e+00
## 736 HUE 6.505451e-02 1.542607590 -1.557563e+00 1.464413e+00 1.565774e+00
## 737 NGRDI -2.190857e-02 0.056443762 -2.370389e-02 -1.214831e-03 9.810347e-03
## 738 Red 1.986211e+02 26.694723101 1.884388e+02 2.043878e+02 2.165204e+02
## 739 SCI 2.190857e-02 0.056443762 -9.810347e-03 1.214831e-03 2.370389e-02
## 740 SI 2.694197e-01 0.059308892 2.421634e-01 2.628703e-01 2.895090e-01
## 741 VARI -3.004305e-02 0.076733608 -3.413615e-02 -1.707213e-03 1.390930e-02
## 742 BGI 6.608370e-01 0.043804392 6.339592e-01 6.554491e-01 6.800178e-01
## 743 BI 1.919331e+02 14.236019575 1.838461e+02 1.934966e+02 2.015874e+02
## 744 Blue 1.387411e+02 15.774465958 1.295306e+02 1.390510e+02 1.479388e+02
## 745 GLI 8.166800e-02 0.017496028 7.120438e-02 8.178244e-02 9.318041e-02
## 746 gray 2.038340e+02 14.767399361 1.957254e+02 2.059767e+02 2.138687e+02
## 747 Green 2.096147e+02 15.624670969 2.016939e+02 2.123469e+02 2.200612e+02
## 748 HI 1.228318e+00 0.243940739 1.068273e+00 1.216686e+00 1.360324e+00
## 749 HUE 1.067478e+00 1.110616393 1.548512e+00 1.563919e+00 1.566450e+00
## 750 NGRDI -1.830239e-02 0.019713099 -2.792836e-02 -1.767597e-02 -5.747744e-03
## 751 Red 2.173034e+02 14.894451577 2.085918e+02 2.181939e+02 2.275102e+02
## 752 SCI 1.830239e-02 0.019713099 5.747744e-03 1.767597e-02 2.792836e-02
## 753 SI 2.223938e-01 0.035662006 2.052356e-01 2.225195e-01 2.423292e-01
## 754 VARI -2.683824e-02 0.028399977 -4.164850e-02 -2.629774e-02 -8.586295e-03
## 755 BGI 4.963050e-01 0.073444184 4.596644e-01 4.764354e-01 5.001154e-01
## 756 BI 1.418642e+02 16.635375975 1.317519e+02 1.386728e+02 1.485321e+02
## 757 Blue 8.440060e+01 20.735227775 7.381633e+01 7.900000e+01 8.697959e+01
## 758 GLI 1.672326e-01 0.029428239 1.573240e-01 1.747358e-01 1.860547e-01
## 759 gray 1.555060e+02 16.393764664 1.453570e+02 1.528568e+02 1.627708e+02
## 760 Green 1.684070e+02 15.653267092 1.587143e+02 1.665918e+02 1.755510e+02
## 761 HI 7.360677e-01 0.120886715 6.470307e-01 6.882540e-01 8.199401e-01
## 762 HUE -1.465058e+00 0.522683401 -1.566622e+00 -1.565708e+00 -1.561889e+00
## 763 NGRDI 3.500862e-02 0.017432143 2.014504e-02 4.046740e-02 4.870738e-02
## 764 Red 1.572891e+02 17.533095896 1.455918e+02 1.540816e+02 1.665714e+02
## 765 SCI -3.500862e-02 0.017432143 -4.870738e-02 -4.046740e-02 -2.014504e-02
## 766 SI 3.083513e-01 0.054457255 2.998362e-01 3.218687e-01 3.385897e-01
## 767 VARI 4.682874e-02 0.022951465 2.751032e-02 5.425122e-02 6.458670e-02
## 768 BGI 5.879011e-01 0.061850301 5.507415e-01 5.762660e-01 6.108035e-01
## 769 BI 1.806679e+02 13.499396994 1.723232e+02 1.811220e+02 1.890370e+02
## 770 Blue 1.188510e+02 18.960246330 1.075408e+02 1.168571e+02 1.272551e+02
## 771 GLI 1.050895e-01 0.018761346 9.529544e-02 1.072040e-01 1.175052e-01
## 772 gray 1.937799e+02 13.618550429 1.852717e+02 1.945903e+02 2.025116e+02
## 773 Green 2.012828e+02 14.193564088 1.925714e+02 2.021837e+02 2.107959e+02
## 774 HI 1.153393e+00 0.178428124 1.021892e+00 1.156526e+00 1.280097e+00
## 775 HUE 8.875292e-01 1.266464458 1.516593e+00 1.563088e+00 1.566288e+00
## 776 NGRDI -1.579716e-02 0.017893962 -2.875175e-02 -1.657241e-02 -2.350120e-03
## 777 Red 2.076182e+02 12.761460651 1.996122e+02 2.081020e+02 2.158673e+02
## 778 SCI 1.579716e-02 0.017893962 2.350120e-03 1.657241e-02 2.875175e-02
## 779 SI 2.757619e-01 0.053393651 2.510746e-01 2.839929e-01 3.098180e-01
## 780 VARI -2.176301e-02 0.024845566 -3.960463e-02 -2.311477e-02 -3.325079e-03
## 781 BGI 5.864472e-01 0.073778484 5.358409e-01 5.760101e-01 6.222618e-01
## 782 BI 1.466735e+02 23.025185723 1.311307e+02 1.484114e+02 1.622069e+02
## 783 Blue 9.536840e+01 24.564633438 7.951020e+01 9.353061e+01 1.083878e+02
## 784 GLI 9.622130e-02 0.060560453 4.670676e-02 1.187224e-01 1.433228e-01
## 785 gray 1.568098e+02 26.091990762 1.402566e+02 1.613986e+02 1.749981e+02
## 786 Green 1.623239e+02 32.050590891 1.453673e+02 1.716122e+02 1.845306e+02
## 787 HI 1.415924e+00 0.925803361 8.165785e-01 9.099307e-01 1.822424e+00
## 788 HUE -3.424832e-01 1.514432586 -1.562261e+00 -1.552045e+00 1.567510e+00
## 789 NGRDI -2.929527e-02 0.075356800 -6.261533e-02 8.569839e-03 2.155741e-02
## 790 Red 1.694101e+02 18.847371714 1.561633e+02 1.688980e+02 1.818776e+02
## 791 SCI 2.929527e-02 0.075356800 -2.155741e-02 -8.569839e-03 6.261533e-02
## 792 SI 2.898002e-01 0.071569873 2.532771e-01 2.851537e-01 3.245449e-01
## 793 VARI -3.936821e-02 0.101748513 -8.886065e-02 1.244002e-02 2.979241e-02
## 794 BGI 6.584637e-01 0.055567861 6.201099e-01 6.504906e-01 6.882962e-01
## 795 BI 1.939397e+02 14.736774250 1.845409e+02 1.949802e+02 2.040201e+02
## 796 Blue 1.413372e+02 18.530469036 1.286837e+02 1.406735e+02 1.527704e+02
## 797 GLI 9.000267e-02 0.018822835 7.859394e-02 9.276735e-02 1.033320e-01
## 798 gray 2.065247e+02 14.496589366 1.975772e+02 2.079326e+02 2.164403e+02
## 799 Green 2.140168e+02 14.112464723 2.056735e+02 2.156633e+02 2.235867e+02
## 800 HI 1.081668e+00 0.115270996 1.003372e+00 1.054054e+00 1.132383e+00
## 801 HUE 8.390084e-01 1.249126982 1.175641e+00 1.544383e+00 1.558319e+00
## 802 NGRDI -6.070114e-03 0.008386717 -1.011802e-02 -4.733728e-03 -2.991629e-04
## 803 Red 2.166702e+02 14.883136897 2.075102e+02 2.180204e+02 2.270765e+02
## 804 SCI 6.070114e-03 0.008386717 2.991629e-04 4.733728e-03 1.011802e-02
## 805 SI 2.130881e-01 0.036944184 1.927199e-01 2.170519e-01 2.374317e-01
## 806 VARI -9.181675e-03 0.012675286 -1.533073e-02 -6.981754e-03 -4.310898e-04
## 807 BGI 5.252285e-01 0.072450206 4.897937e-01 5.116636e-01 5.336676e-01
## 808 BI 1.619602e+02 14.686173970 1.536855e+02 1.618490e+02 1.685587e+02
## 809 Blue 1.000442e+02 20.495266847 8.895918e+01 9.730612e+01 1.049592e+02
## 810 GLI 1.492687e-01 0.025058667 1.444776e-01 1.528915e-01 1.616725e-01
## 811 gray 1.765262e+02 14.432636832 1.682649e+02 1.767711e+02 1.838126e+02
## 812 Green 1.891114e+02 14.083649253 1.807551e+02 1.896735e+02 1.967959e+02
## 813 HI 8.177332e-01 0.080804091 7.378599e-01 8.166704e-01 8.777977e-01
## 814 HUE -1.534086e+00 0.273919485 -1.565714e+00 -1.563684e+00 -1.560046e+00
## 815 NGRDI 2.208385e-02 0.010173725 1.420980e-02 2.185759e-02 3.021689e-02
## 816 Red 1.809793e+02 14.019428440 1.733673e+02 1.815102e+02 1.885102e+02
## 817 SCI -2.208385e-02 0.010173725 -3.021689e-02 -2.185759e-02 -1.420980e-02
## 818 SI 2.940206e-01 0.055638997 2.807332e-01 3.035689e-01 3.251066e-01
## 819 VARI 3.010799e-02 0.013759882 1.942462e-02 2.997483e-02 4.163301e-02
## 820 BGI 6.644490e-01 0.058889432 6.194996e-01 6.552901e-01 7.117511e-01
## 821 BI 1.924538e+02 17.435830759 1.802988e+02 1.918905e+02 2.068018e+02
## 822 Blue 1.413701e+02 21.864834438 1.250408e+02 1.384898e+02 1.598163e+02
## 823 GLI 8.727709e-02 0.018279757 7.260718e-02 8.959835e-02 1.015088e-01
## 824 gray 2.046374e+02 17.179843445 1.928910e+02 2.046483e+02 2.186620e+02
## 825 Green 2.116941e+02 16.922437595 2.002857e+02 2.122857e+02 2.251224e+02
## 826 HI 1.096857e+00 0.103782669 1.025075e+00 1.094980e+00 1.174312e+00
## 827 HUE 1.009635e+00 1.155492078 1.518767e+00 1.554558e+00 1.561193e+00
## 828 NGRDI -7.626938e-03 0.008270597 -1.268319e-02 -8.263614e-03 -2.434299e-03
## 829 Red 2.149054e+02 16.695046967 2.037347e+02 2.147347e+02 2.284286e+02
## 830 SCI 7.626938e-03 0.008270597 2.434299e-03 8.263614e-03 1.268319e-02
## 831 SI 2.103683e-01 0.042225532 1.761900e-01 2.139093e-01 2.414926e-01
## 832 VARI -1.138427e-02 0.012260612 -1.959698e-02 -1.211563e-02 -3.526702e-03
## 833 BGI 5.027097e-01 0.067334059 4.714096e-01 4.927040e-01 5.119752e-01
## 834 BI 1.505484e+02 13.970945293 1.425070e+02 1.513280e+02 1.585097e+02
## 835 Blue 8.919961e+01 18.557558081 7.937245e+01 8.804082e+01 9.519898e+01
## 836 GLI 1.540684e-01 0.025207854 1.450773e-01 1.583017e-01 1.685665e-01
## 837 gray 1.643874e+02 14.214106207 1.560261e+02 1.657379e+02 1.732905e+02
## 838 Green 1.762330e+02 14.988840699 1.673316e+02 1.781020e+02 1.866327e+02
## 839 HI 8.591382e-01 0.146497602 7.342044e-01 8.253012e-01 9.521445e-01
## 840 HUE -9.404625e-01 1.226693297 -1.565763e+00 -1.563230e+00 -1.539686e+00
## 841 NGRDI 1.815431e-02 0.018876351 5.397814e-03 2.246767e-02 3.335130e-02
## 842 Red 1.697989e+02 12.833991451 1.623265e+02 1.709898e+02 1.773827e+02
## 843 SCI -1.815431e-02 0.018876351 -3.335130e-02 -2.246767e-02 -5.397814e-03
## 844 SI 3.170402e-01 0.054824593 3.002515e-01 3.211826e-01 3.471662e-01
## 845 VARI 2.447713e-02 0.025187399 7.542159e-03 3.000085e-02 4.493357e-02
## 846 BGI 5.401996e-01 0.060484950 5.093409e-01 5.291851e-01 5.499103e-01
## 847 BI 1.717271e+02 13.543024645 1.639424e+02 1.711667e+02 1.783935e+02
## 848 Blue 1.086757e+02 18.247335771 9.857143e+01 1.062041e+02 1.140000e+02
## 849 GLI 1.453791e-01 0.021072499 1.404852e-01 1.495929e-01 1.568516e-01
## 850 gray 1.869982e+02 13.364030048 1.792988e+02 1.869496e+02 1.941124e+02
## 851 Green 2.002204e+02 13.033207500 1.927959e+02 2.007755e+02 2.075918e+02
## 852 HI 7.962183e-01 0.065145621 7.199483e-01 8.064516e-01 8.494624e-01
## 853 HUE -1.562826e+00 0.051495215 -1.566236e+00 -1.564793e+00 -1.563266e+00
## 854 NGRDI 2.395892e-02 0.008153070 1.838009e-02 2.299750e-02 3.020949e-02
## 855 Red 1.909022e+02 13.258302149 1.833878e+02 1.904898e+02 1.981633e+02
## 856 SCI -2.395892e-02 0.008153070 -3.020949e-02 -2.299750e-02 -1.838009e-02
## 857 SI 2.785010e-01 0.045531620 2.668452e-01 2.842253e-01 3.037266e-01
## 858 VARI 3.303924e-02 0.011078534 2.513405e-02 3.173461e-02 4.225260e-02
## 859 BGI 5.095070e-01 0.080433409 4.679955e-01 4.899000e-01 5.165715e-01
## 860 BI 1.407327e+02 17.775659232 1.297490e+02 1.366746e+02 1.480760e+02
## 861 Blue 8.506327e+01 22.686944448 7.267857e+01 7.934694e+01 8.964286e+01
## 862 GLI 1.555301e-01 0.028867897 1.491384e-01 1.611795e-01 1.713214e-01
## 863 gray 1.536071e+02 17.583689370 1.424809e+02 1.496647e+02 1.618749e+02
## 864 Green 1.649445e+02 17.174186065 1.539592e+02 1.609592e+02 1.739847e+02
## 865 HI 8.145347e-01 0.119336862 7.205048e-01 8.114187e-01 8.917800e-01
## 866 HUE -1.350941e+00 0.761779914 -1.564849e+00 -1.562290e+00 -1.555784e+00
## 867 NGRDI 2.355922e-02 0.015079984 1.317728e-02 2.407351e-02 3.497375e-02
## 868 Red 1.574831e+02 17.663902522 1.458827e+02 1.546531e+02 1.663418e+02
## 869 SCI -2.355922e-02 0.015079984 -3.497375e-02 -2.407351e-02 -1.317728e-02
## 870 SI 3.072162e-01 0.061912014 2.924830e-01 3.218014e-01 3.437263e-01
## 871 VARI 3.176585e-02 0.020371423 1.780102e-02 3.240083e-02 4.737910e-02
## 872 BGI 6.381156e-01 0.056982616 6.025020e-01 6.348491e-01 6.659326e-01
## 873 BI 1.812765e+02 15.467716855 1.719753e+02 1.828488e+02 1.913189e+02
## 874 Blue 1.293654e+02 19.008309869 1.173673e+02 1.295306e+02 1.402041e+02
## 875 GLI 9.896265e-02 0.020212190 8.684980e-02 9.868596e-02 1.104740e-01
## 876 gray 1.937770e+02 15.291710174 1.846557e+02 1.957343e+02 2.038467e+02
## 877 Green 2.018648e+02 14.938236710 1.931020e+02 2.040000e+02 2.116531e+02
## 878 HI 1.022236e+00 0.118894477 9.538188e-01 1.042996e+00 1.108108e+00
## 879 HUE 3.096260e-01 1.494619069 -1.538058e+00 1.534416e+00 1.555965e+00
## 880 NGRDI -1.339624e-03 0.010780131 -9.105692e-03 -3.662333e-03 4.397402e-03
## 881 Red 2.024572e+02 15.590105378 1.932041e+02 2.047143e+02 2.129388e+02
## 882 SCI 1.339624e-03 0.010780131 -4.397402e-03 3.662333e-03 9.105692e-03
## 883 SI 2.236804e-01 0.039405350 2.036586e-01 2.251856e-01 2.474863e-01
## 884 VARI -2.104214e-03 0.015698941 -1.357954e-02 -5.446467e-03 6.428597e-03
## 885 BGI 5.210521e-01 0.080448550 4.780648e-01 4.984161e-01 5.299018e-01
## 886 BI 1.271799e+02 17.810726833 1.167866e+02 1.235113e+02 1.327078e+02
## 887 Blue 7.827965e+01 21.283298058 6.738776e+01 7.326531e+01 8.123469e+01
## 888 GLI 1.522480e-01 0.041884703 1.490965e-01 1.648938e-01 1.749898e-01
## 889 gray 1.385866e+02 17.576059017 1.280797e+02 1.353327e+02 1.442482e+02
## 890 Green 1.486326e+02 17.443377170 1.383776e+02 1.460204e+02 1.548571e+02
## 891 HI 8.416389e-01 0.455435999 6.749029e-01 7.158597e-01 8.195212e-01
## 892 HUE -1.261554e+00 0.914286777 -1.564590e+00 -1.563423e+00 -1.559197e+00
## 893 NGRDI 2.440967e-02 0.038159032 2.167239e-02 3.658354e-02 4.264516e-02
## 894 Red 1.418575e+02 19.384455288 1.293878e+02 1.372449e+02 1.493367e+02
## 895 SCI -2.440967e-02 0.038159032 -4.264516e-02 -3.658354e-02 -2.167239e-02
## 896 SI 2.963610e-01 0.056637459 2.840955e-01 3.080511e-01 3.255671e-01
## 897 VARI 3.288493e-02 0.052562000 2.941903e-02 4.950771e-02 5.754157e-02
## 898 BGI 5.453743e-01 0.060725423 5.159841e-01 5.383914e-01 5.587139e-01
## 899 BI 1.679055e+02 13.738553642 1.602777e+02 1.689485e+02 1.757478e+02
## 900 Blue 1.062119e+02 17.615024466 9.651020e+01 1.055918e+02 1.134286e+02
## 901 GLI 1.371677e-01 0.021175504 1.299605e-01 1.394883e-01 1.484421e-01
## 902 gray 1.823077e+02 13.864531741 1.746202e+02 1.836658e+02 1.907016e+02
## 903 Green 1.938567e+02 13.973839530 1.860204e+02 1.955306e+02 2.027347e+02
## 904 HI 8.823504e-01 0.106801179 8.050383e-01 8.797092e-01 9.551044e-01
## 905 HUE -1.131290e+00 1.037107801 -1.564435e+00 -1.560164e+00 -1.542158e+00
## 906 NGRDI 1.362842e-02 0.012242606 4.942275e-03 1.379203e-02 2.276527e-02
## 907 Red 1.886478e+02 13.716253402 1.812449e+02 1.902041e+02 1.968980e+02
## 908 SCI -1.362842e-02 0.012242606 -2.276527e-02 -1.379203e-02 -4.942275e-03
## 909 SI 2.835643e-01 0.047315931 2.690711e-01 2.876748e-01 3.082603e-01
## 910 VARI 1.885334e-02 0.016919476 6.899720e-03 1.909397e-02 3.162754e-02
## 911 BGI 6.098708e-01 0.048515839 5.858978e-01 6.034535e-01 6.211456e-01
## 912 BI 1.823296e+02 13.962473545 1.751734e+02 1.834618e+02 1.906842e+02
## 913 Blue 1.242767e+02 16.088114131 1.157551e+02 1.240204e+02 1.318163e+02
## 914 GLI 1.024320e-01 0.017583817 9.683495e-02 1.056287e-01 1.127174e-01
## 915 gray 1.953989e+02 14.446472862 1.882687e+02 1.969838e+02 2.042943e+02
## 916 Green 2.032988e+02 15.191455491 1.962551e+02 2.053265e+02 2.127347e+02
## 917 HI 1.101349e+00 0.190407192 9.750000e-01 1.074074e+00 1.185532e+00
## 918 HUE 6.642535e-01 1.388298867 -1.515729e+00 1.552404e+00 1.563644e+00
## 919 NGRDI -9.330426e-03 0.016570835 -1.789507e-02 -7.035849e-03 2.367722e-03
## 920 Red 2.070067e+02 14.162906822 1.997143e+02 2.080204e+02 2.154898e+02
## 921 SCI 9.330426e-03 0.016570835 -2.367722e-03 7.035849e-03 1.789507e-02
## 922 SI 2.520741e-01 0.038316149 2.395319e-01 2.539443e-01 2.715795e-01
## 923 VARI -1.319924e-02 0.023501379 -2.553830e-02 -1.015334e-02 3.458256e-03
## 924 BGI 5.775280e-01 0.059268366 5.466155e-01 5.684733e-01 5.917077e-01
## 925 BI 1.798084e+02 13.804685710 1.713951e+02 1.805878e+02 1.888358e+02
## 926 Blue 1.160994e+02 17.545167739 1.059337e+02 1.151633e+02 1.240612e+02
## 927 GLI 1.065681e-01 0.019658257 9.628659e-02 1.085298e-01 1.187541e-01
## 928 gray 1.929994e+02 13.980393668 1.846489e+02 1.939929e+02 2.024705e+02
## 929 Green 2.003368e+02 14.118793944 1.922194e+02 2.014286e+02 2.099694e+02
## 930 HI 1.181091e+00 0.170105717 1.070511e+00 1.180301e+00 1.300244e+00
## 931 HUE 1.074290e+00 1.115935823 1.550280e+00 1.563870e+00 1.566862e+00
## 932 NGRDI -1.855927e-02 0.016684623 -3.099037e-02 -1.903932e-02 -6.918338e-03
## 933 Red 2.079141e+02 14.682147471 1.988520e+02 2.091633e+02 2.182857e+02
## 934 SCI 1.855927e-02 0.016684623 6.918338e-03 1.903932e-02 3.099037e-02
## 935 SI 2.864660e-01 0.048097403 2.748939e-01 2.934670e-01 3.118238e-01
## 936 VARI -2.565464e-02 0.023132179 -4.302774e-02 -2.624358e-02 -9.655805e-03
## 937 BGI 4.988361e-01 0.074401554 4.642952e-01 4.861383e-01 5.067953e-01
## 938 BI 1.355695e+02 17.247869426 1.263497e+02 1.341900e+02 1.412329e+02
## 939 Blue 8.162961e+01 21.147861264 7.140816e+01 7.851020e+01 8.481633e+01
## 940 GLI 1.712671e-01 0.030139937 1.637478e-01 1.754849e-01 1.874303e-01
## 941 gray 1.487775e+02 16.999392020 1.394489e+02 1.476704e+02 1.551090e+02
## 942 Green 1.617816e+02 16.236082165 1.527551e+02 1.609184e+02 1.682041e+02
## 943 HI 6.774191e-01 0.121874296 5.974020e-01 6.708747e-01 7.426549e-01
## 944 HUE -1.520831e+00 0.359287687 -1.566691e+00 -1.565826e+00 -1.564166e+00
## 945 NGRDI 4.270895e-02 0.018104473 3.172117e-02 4.405891e-02 5.501470e-02
## 946 Red 1.488492e+02 18.058860357 1.383520e+02 1.476327e+02 1.566327e+02
## 947 SCI -4.270895e-02 0.018104473 -5.501470e-02 -4.405891e-02 -3.172117e-02
## 948 SI 2.991275e-01 0.055117447 2.839054e-01 3.075274e-01 3.302507e-01
## 949 VARI 5.741578e-02 0.023915457 4.302970e-02 5.902586e-02 7.356978e-02
## 950 BGI 6.452998e-01 0.045936947 6.177228e-01 6.405857e-01 6.623352e-01
## 951 BI 1.866355e+02 12.561795684 1.787373e+02 1.879469e+02 1.951089e+02
## 952 Blue 1.335245e+02 14.971982518 1.240816e+02 1.336327e+02 1.418571e+02
## 953 GLI 9.298681e-02 0.014564540 8.694741e-02 9.459459e-02 1.019445e-01
## 954 gray 1.991362e+02 12.779960596 1.912188e+02 2.006840e+02 2.079671e+02
## 955 Green 2.065551e+02 13.098926523 1.984490e+02 2.081633e+02 2.155510e+02
## 956 HI 1.087286e+00 0.096963535 1.027787e+00 1.075134e+00 1.124432e+00
## 957 HUE 1.057341e+00 1.096607559 1.520282e+00 1.551048e+00 1.557895e+00
## 958 NGRDI -7.409015e-03 0.007937124 -1.060440e-02 -6.675982e-03 -2.554079e-03
## 959 Red 2.095872e+02 12.471785917 2.018367e+02 2.112653e+02 2.181735e+02
## 960 SCI 7.409015e-03 0.007937124 2.554079e-03 6.675982e-03 1.060440e-02
## 961 SI 2.235270e-01 0.032708226 2.100503e-01 2.258321e-01 2.434022e-01
## 962 VARI -1.088981e-02 0.011693814 -1.542738e-02 -9.900166e-03 -3.674180e-03
## 963 BGI 4.926107e-01 0.076055516 4.584638e-01 4.762635e-01 4.967913e-01
## 964 BI 1.449054e+02 15.102726740 1.368226e+02 1.435785e+02 1.503030e+02
## 965 Blue 8.557046e+01 20.320668320 7.593878e+01 8.185714e+01 8.810204e+01
## 966 GLI 1.684179e-01 0.029608806 1.596530e-01 1.722198e-01 1.855755e-01
## 967 gray 1.589155e+02 14.771443191 1.510003e+02 1.579749e+02 1.649268e+02
## 968 Green 1.722092e+02 14.180157965 1.646735e+02 1.718163e+02 1.787347e+02
## 969 HI 7.372966e-01 0.110425505 6.428760e-01 7.145973e-01 8.276914e-01
## 970 HUE -1.543911e+00 0.236156202 -1.566825e+00 -1.565429e+00 -1.562419e+00
## 971 NGRDI 3.483607e-02 0.016167620 2.112037e-02 3.589904e-02 4.866875e-02
## 972 Red 1.607814e+02 15.298838110 1.517092e+02 1.598367e+02 1.680663e+02
## 973 SCI -3.483607e-02 0.016167620 -4.866875e-02 -3.589904e-02 -2.112037e-02
## 974 SI 3.121250e-01 0.057069655 3.029319e-01 3.241564e-01 3.414389e-01
## 975 VARI 4.650008e-02 0.021178488 2.848770e-02 4.808487e-02 6.453123e-02
## 976 BGI 5.500808e-01 0.059219728 5.190774e-01 5.385491e-01 5.614153e-01
## 977 BI 1.698625e+02 13.085738901 1.620775e+02 1.697667e+02 1.766736e+02
## 978 Blue 1.077964e+02 17.529332955 9.781633e+01 1.056429e+02 1.135510e+02
## 979 GLI 1.325800e-01 0.020327091 1.250778e-01 1.367276e-01 1.448008e-01
## 980 gray 1.841312e+02 12.984661127 1.763847e+02 1.844599e+02 1.912380e+02
## 981 Green 1.950923e+02 12.850621842 1.874898e+02 1.959184e+02 2.024031e+02
## 982 HI 9.238338e-01 0.096337608 8.541667e-01 9.227202e-01 9.837735e-01
## 983 HUE -9.181693e-01 1.221064614 -1.562356e+00 -1.554486e+00 -1.492483e+00
## 984 NGRDI 8.760708e-03 0.010721637 1.787367e-03 8.883714e-03 1.697813e-02
## 985 Red 1.917166e+02 12.839919611 1.838418e+02 1.917551e+02 1.992398e+02
## 986 SCI -8.760708e-03 0.010721637 -1.697813e-02 -8.883714e-03 -1.787367e-03
## 987 SI 2.839748e-01 0.045120413 2.738891e-01 2.908836e-01 3.082561e-01
## 988 VARI 1.211667e-02 0.014837371 2.454060e-03 1.219110e-02 2.348303e-02
## 989 BGI 5.530584e-01 0.070546213 5.162736e-01 5.370370e-01 5.616927e-01
## 990 BI 1.636566e+02 15.838554294 1.579698e+02 1.650233e+02 1.709376e+02
## 991 Blue 1.023545e+02 19.386269513 9.268367e+01 1.005510e+02 1.077755e+02
## 992 GLI 1.184679e-01 0.033712880 1.154251e-01 1.264874e-01 1.365962e-01
## 993 gray 1.764643e+02 16.981708570 1.711931e+02 1.787695e+02 1.847959e+02
## 994 Green 1.847021e+02 19.405651011 1.799184e+02 1.880612e+02 1.943673e+02
## 995 HI 1.138675e+00 0.415889261 9.411413e-01 1.060355e+00 1.154928e+00
## 996 HUE 4.776940e-01 1.457560413 -1.546702e+00 1.549146e+00 1.562554e+00
## 997 NGRDI -1.195716e-02 0.033378550 -1.785921e-02 -6.830836e-03 6.244229e-03
## 998 Red 1.885476e+02 14.463469695 1.824490e+02 1.896122e+02 1.960204e+02
## 999 SCI 1.195716e-02 0.033378550 -6.244229e-03 6.830836e-03 1.785921e-02
## 1000 SI 3.011107e-01 0.054735114 2.879121e-01 3.094519e-01 3.306392e-01
## 1001 VARI -1.632520e-02 0.046416696 -2.416447e-02 -9.290173e-03 8.861186e-03
## 1002 BGI 5.520141e-01 0.062813968 5.161113e-01 5.406273e-01 5.711589e-01
## 1003 BI 1.704044e+02 14.084085540 1.610973e+02 1.696448e+02 1.790454e+02
## 1004 Blue 1.085908e+02 18.788523149 9.677551e+01 1.058980e+02 1.172857e+02
## 1005 GLI 1.323218e-01 0.020451757 1.255564e-01 1.350708e-01 1.441073e-01
## 1006 gray 1.846741e+02 14.015993053 1.753553e+02 1.842030e+02 1.936099e+02
## 1007 Green 1.956796e+02 13.874220670 1.864490e+02 1.955918e+02 2.048367e+02
## 1008 HI 9.170678e-01 0.084334210 8.595944e-01 9.310345e-01 9.787234e-01
## 1009 HUE -1.011148e+00 1.123121443 -1.562048e+00 -1.553444e+00 -1.516643e+00
## 1010 NGRDI 9.298746e-03 0.009423585 2.538820e-03 8.137282e-03 1.608670e-02
## 1011 Red 1.920764e+02 13.667670877 1.832449e+02 1.917959e+02 2.006939e+02
## 1012 SCI -9.298746e-03 0.009423585 -1.608670e-02 -8.137282e-03 -2.538820e-03
## 1013 SI 2.820562e-01 0.048708887 2.634109e-01 2.886659e-01 3.114409e-01
## 1014 VARI 1.293003e-02 0.013072203 3.470709e-03 1.107428e-02 2.210629e-02
## 1015 BGI 5.256734e-01 0.072080453 4.904160e-01 5.131542e-01 5.387520e-01
## 1016 BI 1.209879e+02 16.247767355 1.118147e+02 1.189464e+02 1.263970e+02
## 1017 Blue 7.622281e+01 19.336986929 6.736735e+01 7.324490e+01 7.971429e+01
## 1018 GLI 1.626774e-01 0.029979247 1.517999e-01 1.672948e-01 1.794422e-01
## 1019 gray 1.323620e+02 15.897821032 1.232632e+02 1.306054e+02 1.381737e+02
## 1020 Green 1.435045e+02 14.965749856 1.349796e+02 1.419184e+02 1.491735e+02
## 1021 HI 6.505509e-01 0.164303956 5.142857e-01 6.686013e-01 7.565742e-01
## 1022 HUE -1.506102e+00 0.403182575 -1.565822e+00 -1.564269e+00 -1.561532e+00
## 1023 NGRDI 4.373226e-02 0.022183631 2.795653e-02 4.204893e-02 6.274719e-02
## 1024 Red 1.318912e+02 17.594686655 1.206122e+02 1.299388e+02 1.397959e+02
## 1025 SCI -4.373226e-02 0.022183631 -6.274719e-02 -4.204893e-02 -2.795653e-02
## 1026 SI 2.736746e-01 0.053357396 2.541083e-01 2.808922e-01 3.038643e-01
## 1027 VARI 6.007716e-02 0.030307970 3.888750e-02 5.708035e-02 8.643853e-02
## 1028 BGI 5.578750e-01 0.060432132 5.296891e-01 5.460820e-01 5.657868e-01
## 1029 BI 1.735987e+02 12.539184577 1.677573e+02 1.740112e+02 1.801916e+02
## 1030 Blue 1.118281e+02 16.923042593 1.034694e+02 1.099592e+02 1.169388e+02
## 1031 GLI 1.329989e-01 0.020295599 1.279804e-01 1.369193e-01 1.437639e-01
## 1032 gray 1.882076e+02 12.446354665 1.826066e+02 1.890148e+02 1.951364e+02
## 1033 Green 1.997972e+02 12.252480796 1.942959e+02 2.009184e+02 2.067959e+02
## 1034 HI 8.807755e-01 0.078175032 8.305580e-01 8.645161e-01 9.274880e-01
## 1035 HUE -1.299025e+00 0.837040105 -1.563329e+00 -1.561718e+00 -1.553874e+00
## 1036 NGRDI 1.336452e-02 0.008493947 8.130490e-03 1.532367e-02 1.875696e-02
## 1037 Red 1.945761e+02 12.667674389 1.888878e+02 1.954286e+02 2.020408e+02
## 1038 SCI -1.336452e-02 0.008493947 -1.875696e-02 -1.532367e-02 -8.130490e-03
## 1039 SI 2.732726e-01 0.044168561 2.656649e-01 2.809121e-01 2.957317e-01
## 1040 VARI 1.858073e-02 0.011769841 1.119082e-02 2.138182e-02 2.583999e-02
## 1041 BGI 5.684252e-01 0.063482799 5.336771e-01 5.566303e-01 5.816006e-01
## 1042 BI 1.675780e+02 14.294929108 1.585796e+02 1.671015e+02 1.749552e+02
## 1043 Blue 1.091996e+02 18.916455546 9.734694e+01 1.065306e+02 1.158367e+02
## 1044 GLI 1.250669e-01 0.021061428 1.184196e-01 1.284107e-01 1.373935e-01
## 1045 gray 1.811104e+02 14.297603350 1.721679e+02 1.810355e+02 1.891966e+02
## 1046 Green 1.911642e+02 14.519567832 1.821020e+02 1.917551e+02 1.998980e+02
## 1047 HI 9.447771e-01 0.109967707 8.652215e-01 9.310345e-01 1.008479e+00
## 1048 HUE -6.989166e-01 1.356095320 -1.560914e+00 -1.552022e+00 1.406263e+00
## 1049 NGRDI 6.067767e-03 0.011733720 -9.096260e-04 7.880770e-03 1.491436e-02
## 1050 Red 1.887903e+02 13.471665480 1.803265e+02 1.885510e+02 1.964898e+02
## 1051 SCI -6.067767e-03 0.011733720 -1.491436e-02 -7.880770e-03 9.096260e-04
## 1052 SI 2.714593e-01 0.047930791 2.563863e-01 2.790943e-01 3.006797e-01
## 1053 VARI 8.537689e-03 0.016324603 -1.285990e-03 1.091068e-02 2.093258e-02
## 1054 BGI 5.538897e-01 0.064125987 5.183250e-01 5.382866e-01 5.640308e-01
## 1055 BI 1.658646e+02 14.583505573 1.572537e+02 1.636151e+02 1.724584e+02
## 1056 Blue 1.066650e+02 19.248572480 9.528571e+01 1.021429e+02 1.123673e+02
## 1057 GLI 1.363094e-01 0.022715745 1.297168e-01 1.413093e-01 1.501980e-01
## 1058 gray 1.799891e+02 14.437187462 1.715123e+02 1.781253e+02 1.867808e+02
## 1059 Green 1.915183e+02 14.304672360 1.831429e+02 1.899592e+02 1.985102e+02
## 1060 HI 8.562103e-01 0.101101371 7.977528e-01 8.248540e-01 9.136449e-01
## 1061 HUE -1.227100e+00 0.939637223 -1.564605e+00 -1.562888e+00 -1.555246e+00
## 1062 NGRDI 1.652142e-02 0.011335245 9.770929e-03 1.884648e-02 2.436599e-02
## 1063 Red 1.853111e+02 14.115508665 1.769184e+02 1.838980e+02 1.926531e+02
## 1064 SCI -1.652142e-02 0.011335245 -2.436599e-02 -1.884648e-02 -9.770929e-03
## 1065 SI 2.739631e-01 0.047236529 2.630824e-01 2.855556e-01 3.012643e-01
## 1066 VARI 2.290355e-02 0.015704551 1.366102e-02 2.663980e-02 3.336905e-02
## 1067 BGI 5.307067e-01 0.079491129 4.908226e-01 5.115637e-01 5.391858e-01
## 1068 BI 1.244050e+02 17.524930639 1.145777e+02 1.205029e+02 1.287368e+02
## 1069 Blue 7.965665e+01 22.016196402 6.855102e+01 7.406122e+01 8.216327e+01
## 1070 GLI 1.657628e-01 0.033222430 1.550760e-01 1.715281e-01 1.864473e-01
## 1071 gray 1.361650e+02 16.929042596 1.263868e+02 1.324612e+02 1.411490e+02
## 1072 Green 1.481796e+02 15.940682445 1.390204e+02 1.443776e+02 1.533367e+02
## 1073 HI 5.915352e-01 0.141259564 4.863329e-01 5.584580e-01 6.771488e-01
## 1074 HUE -1.545667e+00 0.229160113 -1.566612e+00 -1.565777e+00 -1.563666e+00
## 1075 NGRDI 5.114085e-02 0.021219066 3.613851e-02 5.408739e-02 6.786963e-02
## 1076 Red 1.341226e+02 17.920954201 1.229592e+02 1.310612e+02 1.401684e+02
## 1077 SCI -5.114085e-02 0.021219066 -6.786963e-02 -5.408739e-02 -3.613851e-02
## 1078 SI 2.630327e-01 0.054356949 2.480852e-01 2.730803e-01 2.915535e-01
## 1079 VARI 7.041398e-02 0.028409694 5.034989e-02 7.449017e-02 9.261343e-02
## 1080 BGI 5.090541e-01 0.071621979 4.742478e-01 4.918278e-01 5.171235e-01
## 1081 BI 1.546645e+02 13.854847481 1.473701e+02 1.535954e+02 1.598453e+02
## 1082 Blue 9.243691e+01 19.838070394 8.279592e+01 8.853061e+01 9.544388e+01
## 1083 GLI 1.502503e-01 0.025659272 1.416734e-01 1.544467e-01 1.651539e-01
## 1084 gray 1.686061e+02 13.449444388 1.615386e+02 1.679831e+02 1.743067e+02
## 1085 Green 1.802210e+02 12.955885865 1.731633e+02 1.799898e+02 1.859388e+02
## 1086 HI 8.772614e-01 0.125202746 7.808689e-01 8.820645e-01 9.664151e-01
## 1087 HUE -1.048378e+00 1.110732610 -1.564535e+00 -1.559310e+00 -1.530627e+00
## 1088 NGRDI 1.534138e-02 0.015740026 3.931639e-03 1.422708e-02 2.710640e-02
## 1089 Red 1.748448e+02 13.527314646 1.669388e+02 1.746735e+02 1.817500e+02
## 1090 SCI -1.534138e-02 0.015740026 -2.710640e-02 -1.422708e-02 -3.931639e-03
## 1091 SI 3.142759e-01 0.056796849 3.062803e-01 3.269907e-01 3.439769e-01
## 1092 VARI 2.072381e-02 0.021160936 5.361728e-03 1.921964e-02 3.683487e-02
## 1093 BGI 5.005302e-01 0.071717117 4.678733e-01 4.825143e-01 5.007282e-01
## 1094 BI 1.518064e+02 13.519412082 1.438607e+02 1.502683e+02 1.571903e+02
## 1095 Blue 9.037774e+01 19.070757928 8.095918e+01 8.640816e+01 9.316327e+01
## 1096 GLI 1.623223e-01 0.026338556 1.585465e-01 1.683177e-01 1.766429e-01
## 1097 gray 1.662064e+02 13.121014884 1.583341e+02 1.650341e+02 1.720948e+02
## 1098 Green 1.794100e+02 12.543613115 1.719592e+02 1.787143e+02 1.855051e+02
## 1099 HI 7.710813e-01 0.083588834 7.059590e-01 7.467045e-01 8.178881e-01
## 1100 HUE -1.532019e+00 0.291010205 -1.566422e+00 -1.565284e+00 -1.562901e+00
## 1101 NGRDI 2.961158e-02 0.011750087 2.125827e-02 3.067285e-02 3.940839e-02
## 1102 Red 1.691962e+02 13.394407166 1.605153e+02 1.681939e+02 1.759796e+02
## 1103 SCI -2.961158e-02 0.011750087 -3.940839e-02 -3.067285e-02 -2.125827e-02
## 1104 SI 3.091705e-01 0.053402042 3.045200e-01 3.221730e-01 3.352795e-01
## 1105 VARI 3.969639e-02 0.015437608 2.922158e-02 4.142549e-02 5.250423e-02
## 1106 BGI 4.943633e-01 0.077755608 4.569874e-01 4.762338e-01 5.018304e-01
## 1107 BI 1.500091e+02 16.346053182 1.388821e+02 1.478448e+02 1.580539e+02
## 1108 Blue 8.848824e+01 21.326640718 7.678571e+01 8.391837e+01 9.352041e+01
## 1109 GLI 1.644287e-01 0.029371844 1.578504e-01 1.702500e-01 1.807206e-01
## 1110 gray 1.642785e+02 16.145885123 1.530176e+02 1.624805e+02 1.731732e+02
## 1111 Green 1.774038e+02 15.624160188 1.666735e+02 1.760408e+02 1.867245e+02
## 1112 HI 7.767000e-01 0.116010100 6.838015e-01 7.543328e-01 8.530537e-01
## 1113 HUE -1.432486e+00 0.609356500 -1.566493e+00 -1.565250e+00 -1.561781e+00
## 1114 NGRDI 2.952168e-02 0.015972060 1.908002e-02 3.129810e-02 4.170809e-02
## 1115 Red 1.674074e+02 16.832797093 1.552857e+02 1.657755e+02 1.772041e+02
## 1116 SCI -2.952168e-02 0.015972060 -4.170809e-02 -3.129810e-02 -1.908002e-02
## 1117 SI 3.153788e-01 0.058775028 3.015591e-01 3.254717e-01 3.476195e-01
## 1118 VARI 3.938436e-02 0.021129887 2.549999e-02 4.173968e-02 5.583562e-02
## 1119 BGI 4.813056e-01 0.071723892 4.491575e-01 4.649947e-01 4.836746e-01
## 1120 BI 1.381362e+02 15.090175072 1.303772e+02 1.362351e+02 1.420290e+02
## 1121 Blue 8.110110e+01 19.585436620 7.206122e+01 7.751020e+01 8.260204e+01
## 1122 GLI 1.834788e-01 0.028294104 1.819230e-01 1.898909e-01 1.964345e-01
## 1123 gray 1.522604e+02 14.764932821 1.445142e+02 1.507554e+02 1.569166e+02
## 1124 Green 1.669542e+02 13.990913450 1.595000e+02 1.660816e+02 1.721633e+02
## 1125 HI 6.180147e-01 0.050472190 5.743553e-01 6.335472e-01 6.470588e-01
## 1126 HUE -1.566654e+00 0.001409423 -1.567265e+00 -1.566921e+00 -1.566494e+00
## 1127 NGRDI 5.238409e-02 0.010588912 4.844471e-02 5.247905e-02 6.001343e-02
## 1128 Red 1.505443e+02 15.296378341 1.422653e+02 1.485510e+02 1.554898e+02
## 1129 SCI -5.238409e-02 0.010588912 -6.001343e-02 -5.247905e-02 -4.844471e-02
## 1130 SI 3.065535e-01 0.051767056 2.973581e-01 3.166563e-01 3.327070e-01
## 1131 VARI 6.974471e-02 0.013018130 6.419647e-02 6.901146e-02 7.995474e-02
## 1132 BGI 5.181075e-01 0.068363670 4.873843e-01 5.059568e-01 5.272023e-01
## 1133 BI 1.163349e+02 16.070112835 1.088619e+02 1.140037e+02 1.194544e+02
## 1134 Blue 7.468722e+01 18.778085672 6.746939e+01 7.146939e+01 7.546939e+01
## 1135 GLI 1.914194e-01 0.030226781 1.887478e-01 1.971280e-01 2.047002e-01
## 1136 gray 1.283932e+02 15.739239594 1.206865e+02 1.262927e+02 1.322926e+02
## 1137 Green 1.428105e+02 14.813464029 1.352245e+02 1.411224e+02 1.472449e+02
## 1138 HI 3.431670e-01 0.096389971 2.647059e-01 3.538462e-01 4.052184e-01
## 1139 HUE -1.566987e+00 0.001271632 -1.567413e+00 -1.567244e+00 -1.566957e+00
## 1140 NGRDI 8.631994e-02 0.017657830 8.039976e-02 8.675018e-02 9.857390e-02
## 1141 Red 1.205654e+02 17.177782156 1.116122e+02 1.176939e+02 1.251224e+02
## 1142 SCI -8.631994e-02 0.017657830 -9.857390e-02 -8.675018e-02 -8.039976e-02
## 1143 SI 2.404711e-01 0.044615347 2.236065e-01 2.449146e-01 2.644985e-01
## 1144 VARI 1.195189e-01 0.023396586 1.102392e-01 1.186167e-01 1.375628e-01
## 1145 BGI 4.977048e-01 0.071900823 4.658040e-01 4.803104e-01 5.011048e-01
## 1146 BI 1.500779e+02 13.501297581 1.426232e+02 1.483246e+02 1.558315e+02
## 1147 Blue 8.978499e+01 19.156292216 8.053061e+01 8.559184e+01 9.301531e+01
## 1148 GLI 1.707078e-01 0.027092189 1.662892e-01 1.768374e-01 1.846372e-01
## 1149 gray 1.647727e+02 13.013847383 1.573909e+02 1.634205e+02 1.710752e+02
## 1150 Green 1.791654e+02 12.274127833 1.720816e+02 1.782245e+02 1.857755e+02
## 1151 HI 6.850856e-01 0.068881595 6.386950e-01 6.750554e-01 6.842105e-01
## 1152 HUE -1.564609e+00 0.059978148 -1.567163e+00 -1.566854e+00 -1.566082e+00
## 1153 NGRDI 4.124948e-02 0.011068541 3.834929e-02 4.424936e-02 4.884655e-02
## 1154 Red 1.651073e+02 13.317742650 1.571020e+02 1.638878e+02 1.718776e+02
## 1155 SCI -4.124948e-02 0.011068541 -4.884655e-02 -4.424936e-02 -3.834929e-02
## 1156 SI 3.012645e-01 0.052454473 2.927425e-01 3.124210e-01 3.277086e-01
## 1157 VARI 5.540018e-02 0.014143176 5.204514e-02 5.907512e-02 6.517622e-02
## 1158 BGI 5.253363e-01 0.067629035 4.936205e-01 5.123868e-01 5.341050e-01
## 1159 BI 1.611653e+02 13.578761491 1.538553e+02 1.605841e+02 1.673491e+02
## 1160 Blue 9.921314e+01 19.003533597 8.944898e+01 9.644898e+01 1.039592e+02
## 1161 GLI 1.471165e-01 0.024127075 1.394103e-01 1.508226e-01 1.603053e-01
## 1162 gray 1.755543e+02 13.299696296 1.682493e+02 1.753822e+02 1.821610e+02
## 1163 Green 1.877050e+02 12.918404180 1.806735e+02 1.877551e+02 1.946735e+02
## 1164 HI 8.432663e-01 0.116994830 7.273714e-01 8.486218e-01 9.324234e-01
## 1165 HUE -1.256189e+00 0.890806511 -1.565558e+00 -1.562681e+00 -1.552513e+00
## 1166 NGRDI 1.891074e-02 0.014359333 8.185311e-03 1.891443e-02 3.036426e-02
## 1167 Red 1.808066e+02 13.434965099 1.733469e+02 1.805510e+02 1.878980e+02
## 1168 SCI -1.891074e-02 0.014359333 -3.036426e-02 -1.891443e-02 -8.185311e-03
## 1169 SI 2.964117e-01 0.053092933 2.850698e-01 3.045847e-01 3.243282e-01
## 1170 VARI 2.584586e-02 0.019483439 1.112625e-02 2.565829e-02 4.159864e-02
## 1171 BGI 4.909821e-01 0.076433300 4.546185e-01 4.741245e-01 4.946815e-01
## 1172 BI 1.316202e+02 16.319855797 1.219246e+02 1.285912e+02 1.369749e+02
## 1173 Blue 7.893557e+01 20.421924398 6.875510e+01 7.459184e+01 8.120408e+01
## 1174 GLI 1.828602e-01 0.031902343 1.774208e-01 1.898081e-01 1.998493e-01
## 1175 gray 1.449903e+02 15.978123021 1.352068e+02 1.422197e+02 1.511551e+02
## 1176 Green 1.591347e+02 15.143668444 1.496071e+02 1.568367e+02 1.657755e+02
## 1177 HI 5.835302e-01 0.087230938 5.233495e-01 5.755921e-01 6.446344e-01
## 1178 HUE -1.565185e+00 0.056844131 -1.567252e+00 -1.566854e+00 -1.566054e+00
## 1179 NGRDI 5.660431e-02 0.016043690 4.637541e-02 5.825661e-02 6.869171e-02
## 1180 Red 1.424065e+02 17.029333387 1.319592e+02 1.393980e+02 1.489796e+02
## 1181 SCI -5.660431e-02 0.016043690 -6.869171e-02 -5.825661e-02 -4.637541e-02
## 1182 SI 2.940035e-01 0.052286143 2.846082e-01 3.037771e-01 3.223045e-01
## 1183 VARI 7.584273e-02 0.020423375 6.218717e-02 7.792345e-02 9.096605e-02
## 1184 BGI 4.736437e-01 0.072212077 4.361223e-01 4.614656e-01 4.888106e-01
## 1185 BI 1.446258e+02 14.468062144 1.359823e+02 1.436407e+02 1.511702e+02
## 1186 Blue 8.342288e+01 19.322375240 7.302041e+01 8.069388e+01 8.867857e+01
## 1187 GLI 1.833963e-01 0.028569623 1.757036e-01 1.884002e-01 1.992038e-01
## 1188 gray 1.594152e+02 14.231637535 1.507813e+02 1.588291e+02 1.666201e+02
## 1189 Green 1.745284e+02 13.460947280 1.664031e+02 1.743980e+02 1.819184e+02
## 1190 HI 6.532066e-01 0.041206364 6.379420e-01 6.459620e-01 6.774194e-01
## 1191 HUE -1.566672e+00 0.005955030 -1.567267e+00 -1.567020e+00 -1.566662e+00
## 1192 NGRDI 4.800437e-02 0.009296472 4.393461e-02 4.901220e-02 5.308099e-02
## 1193 Red 1.587183e+02 14.569975564 1.494898e+02 1.578980e+02 1.662245e+02
## 1194 SCI -4.800437e-02 0.009296472 -5.308099e-02 -4.901220e-02 -4.393461e-02
## 1195 SI 3.179923e-01 0.055246255 3.010899e-01 3.238763e-01 3.483207e-01
## 1196 VARI 6.348123e-02 0.010905239 5.866035e-02 6.449864e-02 6.898476e-02
## 1197 BGI 5.509069e-01 0.065439595 5.162548e-01 5.366734e-01 5.652501e-01
## 1198 BI 1.575559e+02 14.935285964 1.493091e+02 1.580308e+02 1.656656e+02
## 1199 Blue 9.839235e+01 19.698069087 8.722959e+01 9.608163e+01 1.049745e+02
## 1200 GLI 1.175620e-01 0.021738963 1.072494e-01 1.197379e-01 1.317572e-01
## 1201 gray 1.697889e+02 15.520961712 1.611838e+02 1.708662e+02 1.792393e+02
## 1202 Green 1.774054e+02 17.056276178 1.672908e+02 1.789796e+02 1.888980e+02
## 1203 HI 1.127089e+00 0.213384041 9.629172e-01 1.138308e+00 1.271128e+00
## 1204 HUE 6.361729e-01 1.410905041 -1.534243e+00 1.560143e+00 1.565094e+00
## 1205 NGRDI -1.408290e-02 0.023231293 -3.004887e-02 -1.500344e-02 4.101386e-03
## 1206 Red 1.820576e+02 12.774441026 1.753316e+02 1.827347e+02 1.891837e+02
## 1207 SCI 1.408290e-02 0.023231293 -4.101386e-03 1.500344e-02 3.004887e-02
## 1208 SI 3.041629e-01 0.058549704 2.835034e-01 3.125746e-01 3.388524e-01
## 1209 VARI -1.875302e-02 0.031458960 -4.064677e-02 -2.066285e-02 5.830924e-03
## 1210 BGI 4.603501e-01 0.066690415 4.293498e-01 4.464923e-01 4.688840e-01
## 1211 BI 1.369996e+02 12.903926346 1.303857e+02 1.353736e+02 1.406537e+02
## 1212 Blue 7.717352e+01 17.128535412 6.948980e+01 7.432653e+01 7.971429e+01
## 1213 GLI 1.907362e-01 0.027658058 1.854010e-01 1.970940e-01 2.050209e-01
## 1214 gray 1.513755e+02 12.533123287 1.447472e+02 1.500102e+02 1.555212e+02
## 1215 Green 1.664320e+02 11.542724972 1.603265e+02 1.656122e+02 1.709388e+02
## 1216 HI 6.338923e-01 0.090799056 5.515404e-01 6.348096e-01 6.772152e-01
## 1217 HUE -1.548845e+00 0.229626368 -1.567450e+00 -1.567049e+00 -1.566466e+00
## 1218 NGRDI 5.237126e-02 0.014917541 4.633257e-02 5.440053e-02 6.384365e-02
## 1219 Red 1.501077e+02 13.853210365 1.422857e+02 1.477755e+02 1.547959e+02
## 1220 SCI -5.237126e-02 0.014917541 -6.384365e-02 -5.440053e-02 -4.633257e-02
## 1221 SI 3.263082e-01 0.050926985 3.113899e-01 3.341239e-01 3.550033e-01
## 1222 VARI 6.881538e-02 0.019124398 6.061852e-02 7.061930e-02 8.483172e-02
## 1223 BGI 4.945712e-01 0.072534243 4.608673e-01 4.801561e-01 5.029688e-01
## 1224 BI 1.355914e+02 15.327820824 1.282724e+02 1.341147e+02 1.408786e+02
## 1225 Blue 8.048875e+01 19.806457644 7.151020e+01 7.730612e+01 8.369388e+01
## 1226 GLI 1.677679e-01 0.028555953 1.594224e-01 1.741036e-01 1.847201e-01
## 1227 gray 1.486683e+02 15.112120934 1.414012e+02 1.476821e+02 1.546591e+02
## 1228 Green 1.610853e+02 14.800418939 1.540408e+02 1.610612e+02 1.678367e+02
## 1229 HI 7.347673e-01 0.140129855 6.384710e-01 6.915866e-01 8.122867e-01
## 1230 HUE -1.383908e+00 0.711218484 -1.566389e+00 -1.565415e+00 -1.561489e+00
## 1231 NGRDI 3.506482e-02 0.019472248 2.233800e-02 3.980236e-02 4.909028e-02
## 1232 Red 1.502860e+02 15.355172926 1.422245e+02 1.482857e+02 1.566327e+02
## 1233 SCI -3.506482e-02 0.019472248 -4.909028e-02 -3.980236e-02 -2.233800e-02
## 1234 SI 3.096930e-01 0.056438616 2.932626e-01 3.152297e-01 3.387922e-01
## 1235 VARI 4.701988e-02 0.025845249 3.067382e-02 5.345882e-02 6.551386e-02
## 1236 BGI 4.833572e-01 0.076549012 4.456626e-01 4.703528e-01 4.968566e-01
## 1237 BI 1.332715e+02 16.075478052 1.247668e+02 1.311760e+02 1.386993e+02
## 1238 Blue 7.767911e+01 20.781556650 6.732653e+01 7.412245e+01 8.144898e+01
## 1239 GLI 1.719859e-01 0.029334902 1.630791e-01 1.766150e-01 1.883165e-01
## 1240 gray 1.462727e+02 15.829454682 1.378049e+02 1.446074e+02 1.523001e+02
## 1241 Green 1.587213e+02 15.267577895 1.504490e+02 1.576327e+02 1.653265e+02
## 1242 HI 7.344640e-01 0.141404219 6.357214e-01 7.025882e-01 8.295425e-01
## 1243 HUE -1.397312e+00 0.682988251 -1.566325e+00 -1.565095e+00 -1.560846e+00
## 1244 NGRDI 3.562392e-02 0.019778881 2.172519e-02 3.921069e-02 4.992249e-02
## 1245 Red 1.479862e+02 16.363214380 1.385918e+02 1.457551e+02 1.546327e+02
## 1246 SCI -3.562392e-02 0.019778881 -4.992249e-02 -3.921069e-02 -2.172519e-02
## 1247 SI 3.199480e-01 0.062886283 2.954565e-01 3.264114e-01 3.563706e-01
## 1248 VARI 4.751978e-02 0.026222598 2.910383e-02 5.226047e-02 6.626938e-02
## 1249 BGI 4.891723e-01 0.077124038 4.502185e-01 4.718030e-01 4.980867e-01
## 1250 BI 1.511301e+02 15.085195539 1.423862e+02 1.496438e+02 1.573360e+02
## 1251 Blue 8.899860e+01 21.120474811 7.759184e+01 8.475510e+01 9.359184e+01
## 1252 GLI 1.718740e-01 0.028383481 1.672119e-01 1.778123e-01 1.866563e-01
## 1253 gray 1.659428e+02 14.691293244 1.572901e+02 1.649439e+02 1.727401e+02
## 1254 Green 1.802973e+02 14.015851600 1.719796e+02 1.797551e+02 1.876735e+02
## 1255 HI 7.107380e-01 0.049556763 6.790805e-01 6.962406e-01 7.156105e-01
## 1256 HUE -1.565891e+00 0.002488825 -1.566880e+00 -1.566620e+00 -1.565975e+00
## 1257 NGRDI 3.833134e-02 0.008556742 3.585884e-02 4.100876e-02 4.359573e-02
## 1258 Red 1.670983e+02 14.538445320 1.586327e+02 1.662041e+02 1.738980e+02
## 1259 SCI -3.833134e-02 0.008556742 -4.359573e-02 -4.100876e-02 -3.585884e-02
## 1260 SI 3.123070e-01 0.057964673 2.994342e-01 3.240924e-01 3.440454e-01
## 1261 VARI 5.109758e-02 0.010514036 4.851043e-02 5.457916e-02 5.762141e-02
## 1262 BGI 5.497641e-01 0.071990678 5.051916e-01 5.345074e-01 5.746432e-01
## 1263 BI 1.592227e+02 15.343947347 1.515961e+02 1.589253e+02 1.661636e+02
## 1264 Blue 9.815995e+01 20.578510863 8.644898e+01 9.457143e+01 1.044694e+02
## 1265 GLI 1.106292e-01 0.025172084 1.020395e-01 1.158511e-01 1.265604e-01
## 1266 gray 1.710800e+02 15.796697661 1.636931e+02 1.713225e+02 1.786709e+02
## 1267 Green 1.774539e+02 16.914863703 1.702653e+02 1.781837e+02 1.857143e+02
## 1268 HI 1.240187e+00 0.268503292 1.141919e+00 1.215232e+00 1.304976e+00
## 1269 HUE 1.223562e+00 0.964983504 1.561161e+00 1.564360e+00 1.566215e+00
## 1270 NGRDI -2.556116e-02 0.023643060 -3.449972e-02 -2.424276e-02 -1.622224e-02
## 1271 Red 1.863689e+02 13.917131854 1.789592e+02 1.869184e+02 1.942245e+02
## 1272 SCI 2.556116e-02 0.023643060 1.622224e-02 2.424276e-02 3.449972e-02
## 1273 SI 3.160340e-01 0.061573889 2.956308e-01 3.276614e-01 3.538531e-01
## 1274 VARI -3.446801e-02 0.032521555 -4.624071e-02 -3.279446e-02 -2.202713e-02
## 1275 BGI 5.551954e-01 0.060746322 5.231954e-01 5.460024e-01 5.712243e-01
## 1276 BI 1.661646e+02 14.931612821 1.569303e+02 1.664568e+02 1.747483e+02
## 1277 Blue 1.042867e+02 19.142136682 9.310204e+01 1.024898e+02 1.119184e+02
## 1278 GLI 1.156305e-01 0.019603682 1.054500e-01 1.165262e-01 1.280371e-01
## 1279 gray 1.789397e+02 15.175842212 1.695171e+02 1.797403e+02 1.882576e+02
## 1280 Green 1.866665e+02 15.709198703 1.768163e+02 1.877347e+02 1.970918e+02
## 1281 HI 1.136177e+00 0.179695391 1.006772e+00 1.152008e+00 1.262136e+00
## 1282 HUE 8.152243e-01 1.315047653 1.375514e+00 1.561724e+00 1.565548e+00
## 1283 NGRDI -1.503247e-02 0.019851187 -2.870132e-02 -1.640505e-02 -7.652383e-04
## 1284 Red 1.922333e+02 14.629418358 1.832245e+02 1.923265e+02 2.014898e+02
## 1285 SCI 1.503247e-02 0.019851187 7.652383e-04 1.640505e-02 2.870132e-02
## 1286 SI 3.012859e-01 0.053649145 2.808378e-01 3.067718e-01 3.314434e-01
## 1287 VARI -2.023670e-02 0.026859109 -3.897291e-02 -2.256197e-02 -1.041628e-03
## 1288 BGI 5.782218e-01 0.073713618 5.328897e-01 5.679634e-01 6.076278e-01
## 1289 BI 1.531340e+02 20.303029478 1.416983e+02 1.542746e+02 1.660485e+02
## 1290 Blue 9.713375e+01 22.574468320 8.230612e+01 9.493878e+01 1.087143e+02
## 1291 GLI 8.987709e-02 0.031091971 7.255340e-02 9.339408e-02 1.115092e-01
## 1292 gray 1.632016e+02 21.773797084 1.513917e+02 1.650409e+02 1.775392e+02
## 1293 Green 1.668868e+02 24.228789246 1.538980e+02 1.691633e+02 1.834592e+02
## 1294 HI 1.458850e+00 0.447086283 1.158589e+00 1.417849e+00 1.683580e+00
## 1295 HUE 1.107249e+00 1.096934808 1.560294e+00 1.566651e+00 1.568067e+00
## 1296 NGRDI -4.370844e-02 0.039046882 -6.815533e-02 -4.256342e-02 -1.584418e-02
## 1297 Red 1.811563e+02 19.653342033 1.704694e+02 1.823878e+02 1.941020e+02
## 1298 SCI 4.370844e-02 0.039046882 1.584418e-02 4.256342e-02 6.815533e-02
## 1299 SI 3.092505e-01 0.068714780 2.721891e-01 3.173554e-01 3.538793e-01
## 1300 VARI -5.934955e-02 0.052935406 -9.209065e-02 -5.845817e-02 -2.228588e-02
## 1301 BGI 4.578249e-01 0.069788671 4.253275e-01 4.455446e-01 4.685013e-01
## 1302 BI 1.394011e+02 13.902080948 1.307526e+02 1.376127e+02 1.459149e+02
## 1303 Blue 7.836742e+01 18.221380826 6.916327e+01 7.551020e+01 8.267347e+01
## 1304 GLI 1.932495e-01 0.028192742 1.870605e-01 1.981824e-01 2.075384e-01
## 1305 gray 1.541359e+02 13.670509813 1.453141e+02 1.525849e+02 1.613091e+02
## 1306 Green 1.697594e+02 12.891990425 1.612857e+02 1.686939e+02 1.772041e+02
## 1307 HI 6.182237e-01 0.057107968 5.581872e-01 6.381407e-01 6.518657e-01
## 1308 HUE -1.567001e+00 0.002008998 -1.567533e+00 -1.567223e+00 -1.566934e+00
## 1309 NGRDI 5.476766e-02 0.011487292 4.871060e-02 5.427046e-02 6.345700e-02
## 1310 Red 1.523520e+02 14.447575699 1.427551e+02 1.505102e+02 1.602857e+02
## 1311 SCI -5.476766e-02 0.011487292 -6.345700e-02 -5.427046e-02 -4.871060e-02
## 1312 SI 3.269492e-01 0.052396085 3.131143e-01 3.343954e-01 3.537519e-01
## 1313 VARI 7.183425e-02 0.014044639 6.398123e-02 7.022425e-02 8.389617e-02
## 1314 BGI 5.433003e-01 0.080509769 5.003178e-01 5.260650e-01 5.603382e-01
## 1315 BI 1.229925e+02 22.676941957 1.083390e+02 1.180642e+02 1.339646e+02
## 1316 Blue 7.443487e+01 24.700586500 6.047449e+01 6.772449e+01 7.991837e+01
## 1317 GLI 1.002194e-01 0.028803679 8.636963e-02 1.018091e-01 1.184094e-01
## 1318 gray 1.315358e+02 24.166978328 1.156010e+02 1.263858e+02 1.441172e+02
## 1319 Green 1.351380e+02 26.769994709 1.171224e+02 1.293980e+02 1.495714e+02
## 1320 HI 1.412909e+00 0.343236318 1.213558e+00 1.441452e+00 1.625032e+00
## 1321 HUE 1.141374e+00 1.061235025 1.560271e+00 1.565005e+00 1.566374e+00
## 1322 NGRDI -4.408670e-02 0.034088184 -6.872650e-02 -4.979960e-02 -2.280517e-02
## 1323 Red 1.462348e+02 20.249014525 1.331224e+02 1.430408e+02 1.577347e+02
## 1324 SCI 4.408670e-02 0.034088184 2.280517e-02 4.979960e-02 6.872650e-02
## 1325 SI 3.380381e-01 0.073130513 3.153315e-01 3.549401e-01 3.811566e-01
## 1326 VARI -5.851747e-02 0.045732819 -9.082683e-02 -6.616031e-02 -3.099298e-02
## 1327 BGI 4.950562e-01 0.075654157 4.563590e-01 4.833557e-01 5.115477e-01
## 1328 BI 1.306872e+02 17.507306667 1.202224e+02 1.289388e+02 1.384521e+02
## 1329 Blue 7.607665e+01 21.258945133 6.471429e+01 7.250000e+01 8.136735e+01
## 1330 GLI 1.493519e-01 0.031952196 1.302149e-01 1.524699e-01 1.723624e-01
## 1331 gray 1.423360e+02 18.051337428 1.308762e+02 1.407138e+02 1.516943e+02
## 1332 Green 1.516844e+02 19.328922610 1.383418e+02 1.501020e+02 1.637806e+02
## 1333 HI 9.545467e-01 0.229851933 7.446127e-01 9.392129e-01 1.141102e+00
## 1334 HUE -2.372749e-01 1.524993615 -1.564403e+00 -1.540947e+00 1.556133e+00
## 1335 NGRDI 6.915608e-03 0.029070267 -1.673489e-02 7.660257e-03 3.204692e-02
## 1336 Red 1.492458e+02 16.165977805 1.402449e+02 1.480408e+02 1.563724e+02
## 1337 SCI -6.915608e-03 0.029070267 -3.204692e-02 -7.660257e-03 1.673489e-02
## 1338 SI 3.345940e-01 0.063954079 3.116501e-01 3.420964e-01 3.707661e-01
## 1339 VARI 9.392848e-03 0.038543776 -2.224237e-02 1.030962e-02 4.327312e-02
## 1340 BGI 4.825232e-01 0.064109709 4.487681e-01 4.695543e-01 4.968354e-01
## 1341 BI 1.589125e+02 14.604421080 1.490106e+02 1.565937e+02 1.667296e+02
## 1342 Blue 9.097271e+01 17.795859455 8.085714e+01 8.736735e+01 9.642857e+01
## 1343 GLI 1.638030e-01 0.024559347 1.539416e-01 1.678815e-01 1.793575e-01
## 1344 gray 1.740549e+02 14.628133723 1.640534e+02 1.719860e+02 1.822361e+02
## 1345 Green 1.874722e+02 13.953248417 1.780204e+02 1.859388e+02 1.955306e+02
## 1346 HI 8.278611e-01 0.142537201 7.082585e-01 8.025809e-01 9.149619e-01
## 1347 HUE -1.132381e+00 1.066448924 -1.566595e+00 -1.564801e+00 -1.557562e+00
## 1348 NGRDI 2.281530e-02 0.019125184 1.083953e-02 2.537253e-02 3.903968e-02
## 1349 Red 1.793906e+02 16.886961273 1.672857e+02 1.768980e+02 1.891429e+02
## 1350 SCI -2.281530e-02 0.019125184 -3.903968e-02 -2.537253e-02 -1.083953e-02
## 1351 SI 3.311142e-01 0.054800879 3.115736e-01 3.378747e-01 3.635422e-01
## 1352 VARI 3.042245e-02 0.025250191 1.462237e-02 3.390225e-02 5.215949e-02
## 1353 BGI 5.079571e-01 0.069477455 4.733950e-01 4.951851e-01 5.198283e-01
## 1354 BI 1.443206e+02 15.621134529 1.345758e+02 1.435821e+02 1.523447e+02
## 1355 Blue 8.561669e+01 19.784450282 7.467347e+01 8.279592e+01 9.114286e+01
## 1356 GLI 1.450213e-01 0.025913335 1.331806e-01 1.481585e-01 1.618094e-01
## 1357 gray 1.570285e+02 15.900702841 1.469231e+02 1.567712e+02 1.661186e+02
## 1358 Green 1.670401e+02 16.587756186 1.564082e+02 1.673265e+02 1.774592e+02
## 1359 HI 9.460817e-01 0.183930315 7.989478e-01 9.359734e-01 1.084247e+00
## 1360 HUE -3.656883e-01 1.497186552 -1.563539e+00 -1.547094e+00 1.551803e+00
## 1361 NGRDI 6.913620e-03 0.022677370 -9.986760e-03 7.851718e-03 2.507888e-02
## 1362 Red 1.646008e+02 14.992481853 1.548980e+02 1.640612e+02 1.731837e+02
## 1363 SCI -6.913620e-03 0.022677370 -2.507888e-02 -7.851718e-03 9.986760e-03
## 1364 SI 3.224781e-01 0.058898945 3.040703e-01 3.306876e-01 3.551841e-01
## 1365 VARI 9.564455e-03 0.030336141 -1.343971e-02 1.055493e-02 3.411655e-02
## file ImageJ LeafDoctor APSAssess sev
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## 1044 60 32.4500000 33.21 32.29 32.4500000
## 1045 60 32.4500000 33.21 32.29 32.4500000
## 1046 60 32.4500000 33.21 32.29 32.4500000
## 1047 60 32.4500000 33.21 32.29 32.4500000
## 1048 60 32.4500000 33.21 32.29 32.4500000
## 1049 60 32.4500000 33.21 32.29 32.4500000
## 1050 60 32.4500000 33.21 32.29 32.4500000
## 1051 60 32.4500000 33.21 32.29 32.4500000
## 1052 60 32.4500000 33.21 32.29 32.4500000
## 1053 60 32.4500000 33.21 32.29 32.4500000
## 1054 61 23.6400000 23.75 27.97 23.6400000
## 1055 61 23.6400000 23.75 27.97 23.6400000
## 1056 61 23.6400000 23.75 27.97 23.6400000
## 1057 61 23.6400000 23.75 27.97 23.6400000
## 1058 61 23.6400000 23.75 27.97 23.6400000
## 1059 61 23.6400000 23.75 27.97 23.6400000
## 1060 61 23.6400000 23.75 27.97 23.6400000
## 1061 61 23.6400000 23.75 27.97 23.6400000
## 1062 61 23.6400000 23.75 27.97 23.6400000
## 1063 61 23.6400000 23.75 27.97 23.6400000
## 1064 61 23.6400000 23.75 27.97 23.6400000
## 1065 61 23.6400000 23.75 27.97 23.6400000
## 1066 61 23.6400000 23.75 27.97 23.6400000
## 1067 62 13.1000000 13.91 13.28 13.1000000
## 1068 62 13.1000000 13.91 13.28 13.1000000
## 1069 62 13.1000000 13.91 13.28 13.1000000
## 1070 62 13.1000000 13.91 13.28 13.1000000
## 1071 62 13.1000000 13.91 13.28 13.1000000
## 1072 62 13.1000000 13.91 13.28 13.1000000
## 1073 62 13.1000000 13.91 13.28 13.1000000
## 1074 62 13.1000000 13.91 13.28 13.1000000
## 1075 62 13.1000000 13.91 13.28 13.1000000
## 1076 62 13.1000000 13.91 13.28 13.1000000
## 1077 62 13.1000000 13.91 13.28 13.1000000
## 1078 62 13.1000000 13.91 13.28 13.1000000
## 1079 62 13.1000000 13.91 13.28 13.1000000
## 1080 63 49.8000000 49.86 48.71 49.8000000
## 1081 63 49.8000000 49.86 48.71 49.8000000
## 1082 63 49.8000000 49.86 48.71 49.8000000
## 1083 63 49.8000000 49.86 48.71 49.8000000
## 1084 63 49.8000000 49.86 48.71 49.8000000
## 1085 63 49.8000000 49.86 48.71 49.8000000
## 1086 63 49.8000000 49.86 48.71 49.8000000
## 1087 63 49.8000000 49.86 48.71 49.8000000
## 1088 63 49.8000000 49.86 48.71 49.8000000
## 1089 63 49.8000000 49.86 48.71 49.8000000
## 1090 63 49.8000000 49.86 48.71 49.8000000
## 1091 63 49.8000000 49.86 48.71 49.8000000
## 1092 63 49.8000000 49.86 48.71 49.8000000
## 1093 64 11.6500000 12.31 10.33 11.6500000
## 1094 64 11.6500000 12.31 10.33 11.6500000
## 1095 64 11.6500000 12.31 10.33 11.6500000
## 1096 64 11.6500000 12.31 10.33 11.6500000
## 1097 64 11.6500000 12.31 10.33 11.6500000
## 1098 64 11.6500000 12.31 10.33 11.6500000
## 1099 64 11.6500000 12.31 10.33 11.6500000
## 1100 64 11.6500000 12.31 10.33 11.6500000
## 1101 64 11.6500000 12.31 10.33 11.6500000
## 1102 64 11.6500000 12.31 10.33 11.6500000
## 1103 64 11.6500000 12.31 10.33 11.6500000
## 1104 64 11.6500000 12.31 10.33 11.6500000
## 1105 64 11.6500000 12.31 10.33 11.6500000
## 1106 65 18.3600000 18.62 16.06 18.3600000
## 1107 65 18.3600000 18.62 16.06 18.3600000
## 1108 65 18.3600000 18.62 16.06 18.3600000
## 1109 65 18.3600000 18.62 16.06 18.3600000
## 1110 65 18.3600000 18.62 16.06 18.3600000
## 1111 65 18.3600000 18.62 16.06 18.3600000
## 1112 65 18.3600000 18.62 16.06 18.3600000
## 1113 65 18.3600000 18.62 16.06 18.3600000
## 1114 65 18.3600000 18.62 16.06 18.3600000
## 1115 65 18.3600000 18.62 16.06 18.3600000
## 1116 65 18.3600000 18.62 16.06 18.3600000
## 1117 65 18.3600000 18.62 16.06 18.3600000
## 1118 65 18.3600000 18.62 16.06 18.3600000
## 1119 68 2.6400000 2.55 3.38 2.6400000
## 1120 68 2.6400000 2.55 3.38 2.6400000
## 1121 68 2.6400000 2.55 3.38 2.6400000
## 1122 68 2.6400000 2.55 3.38 2.6400000
## 1123 68 2.6400000 2.55 3.38 2.6400000
## 1124 68 2.6400000 2.55 3.38 2.6400000
## 1125 68 2.6400000 2.55 3.38 2.6400000
## 1126 68 2.6400000 2.55 3.38 2.6400000
## 1127 68 2.6400000 2.55 3.38 2.6400000
## 1128 68 2.6400000 2.55 3.38 2.6400000
## 1129 68 2.6400000 2.55 3.38 2.6400000
## 1130 68 2.6400000 2.55 3.38 2.6400000
## 1131 68 2.6400000 2.55 3.38 2.6400000
## 1132 7 3.7800000 3.30 3.11 3.7800000
## 1133 7 3.7800000 3.30 3.11 3.7800000
## 1134 7 3.7800000 3.30 3.11 3.7800000
## 1135 7 3.7800000 3.30 3.11 3.7800000
## 1136 7 3.7800000 3.30 3.11 3.7800000
## 1137 7 3.7800000 3.30 3.11 3.7800000
## 1138 7 3.7800000 3.30 3.11 3.7800000
## 1139 7 3.7800000 3.30 3.11 3.7800000
## 1140 7 3.7800000 3.30 3.11 3.7800000
## 1141 7 3.7800000 3.30 3.11 3.7800000
## 1142 7 3.7800000 3.30 3.11 3.7800000
## 1143 7 3.7800000 3.30 3.11 3.7800000
## 1144 7 3.7800000 3.30 3.11 3.7800000
## 1145 70 11.7900000 12.52 13.70 11.7900000
## 1146 70 11.7900000 12.52 13.70 11.7900000
## 1147 70 11.7900000 12.52 13.70 11.7900000
## 1148 70 11.7900000 12.52 13.70 11.7900000
## 1149 70 11.7900000 12.52 13.70 11.7900000
## 1150 70 11.7900000 12.52 13.70 11.7900000
## 1151 70 11.7900000 12.52 13.70 11.7900000
## 1152 70 11.7900000 12.52 13.70 11.7900000
## 1153 70 11.7900000 12.52 13.70 11.7900000
## 1154 70 11.7900000 12.52 13.70 11.7900000
## 1155 70 11.7900000 12.52 13.70 11.7900000
## 1156 70 11.7900000 12.52 13.70 11.7900000
## 1157 70 11.7900000 12.52 13.70 11.7900000
## 1158 72 37.8300000 38.33 36.45 37.8300000
## 1159 72 37.8300000 38.33 36.45 37.8300000
## 1160 72 37.8300000 38.33 36.45 37.8300000
## 1161 72 37.8300000 38.33 36.45 37.8300000
## 1162 72 37.8300000 38.33 36.45 37.8300000
## 1163 72 37.8300000 38.33 36.45 37.8300000
## 1164 72 37.8300000 38.33 36.45 37.8300000
## 1165 72 37.8300000 38.33 36.45 37.8300000
## 1166 72 37.8300000 38.33 36.45 37.8300000
## 1167 72 37.8300000 38.33 36.45 37.8300000
## 1168 72 37.8300000 38.33 36.45 37.8300000
## 1169 72 37.8300000 38.33 36.45 37.8300000
## 1170 72 37.8300000 38.33 36.45 37.8300000
## 1171 73 38.4500000 39.29 35.31 38.4500000
## 1172 73 38.4500000 39.29 35.31 38.4500000
## 1173 73 38.4500000 39.29 35.31 38.4500000
## 1174 73 38.4500000 39.29 35.31 38.4500000
## 1175 73 38.4500000 39.29 35.31 38.4500000
## 1176 73 38.4500000 39.29 35.31 38.4500000
## 1177 73 38.4500000 39.29 35.31 38.4500000
## 1178 73 38.4500000 39.29 35.31 38.4500000
## 1179 73 38.4500000 39.29 35.31 38.4500000
## 1180 73 38.4500000 39.29 35.31 38.4500000
## 1181 73 38.4500000 39.29 35.31 38.4500000
## 1182 73 38.4500000 39.29 35.31 38.4500000
## 1183 73 38.4500000 39.29 35.31 38.4500000
## 1184 74 1.2600000 1.37 1.76 1.2600000
## 1185 74 1.2600000 1.37 1.76 1.2600000
## 1186 74 1.2600000 1.37 1.76 1.2600000
## 1187 74 1.2600000 1.37 1.76 1.2600000
## 1188 74 1.2600000 1.37 1.76 1.2600000
## 1189 74 1.2600000 1.37 1.76 1.2600000
## 1190 74 1.2600000 1.37 1.76 1.2600000
## 1191 74 1.2600000 1.37 1.76 1.2600000
## 1192 74 1.2600000 1.37 1.76 1.2600000
## 1193 74 1.2600000 1.37 1.76 1.2600000
## 1194 74 1.2600000 1.37 1.76 1.2600000
## 1195 74 1.2600000 1.37 1.76 1.2600000
## 1196 74 1.2600000 1.37 1.76 1.2600000
## 1197 75 71.5100000 72.21 68.56 71.5100000
## 1198 75 71.5100000 72.21 68.56 71.5100000
## 1199 75 71.5100000 72.21 68.56 71.5100000
## 1200 75 71.5100000 72.21 68.56 71.5100000
## 1201 75 71.5100000 72.21 68.56 71.5100000
## 1202 75 71.5100000 72.21 68.56 71.5100000
## 1203 75 71.5100000 72.21 68.56 71.5100000
## 1204 75 71.5100000 72.21 68.56 71.5100000
## 1205 75 71.5100000 72.21 68.56 71.5100000
## 1206 75 71.5100000 72.21 68.56 71.5100000
## 1207 75 71.5100000 72.21 68.56 71.5100000
## 1208 75 71.5100000 72.21 68.56 71.5100000
## 1209 75 71.5100000 72.21 68.56 71.5100000
## 1210 76 10.1100000 11.60 8.80 10.1100000
## 1211 76 10.1100000 11.60 8.80 10.1100000
## 1212 76 10.1100000 11.60 8.80 10.1100000
## 1213 76 10.1100000 11.60 8.80 10.1100000
## 1214 76 10.1100000 11.60 8.80 10.1100000
## 1215 76 10.1100000 11.60 8.80 10.1100000
## 1216 76 10.1100000 11.60 8.80 10.1100000
## 1217 76 10.1100000 11.60 8.80 10.1100000
## 1218 76 10.1100000 11.60 8.80 10.1100000
## 1219 76 10.1100000 11.60 8.80 10.1100000
## 1220 76 10.1100000 11.60 8.80 10.1100000
## 1221 76 10.1100000 11.60 8.80 10.1100000
## 1222 76 10.1100000 11.60 8.80 10.1100000
## 1223 77 29.0800000 29.99 35.11 29.0800000
## 1224 77 29.0800000 29.99 35.11 29.0800000
## 1225 77 29.0800000 29.99 35.11 29.0800000
## 1226 77 29.0800000 29.99 35.11 29.0800000
## 1227 77 29.0800000 29.99 35.11 29.0800000
## 1228 77 29.0800000 29.99 35.11 29.0800000
## 1229 77 29.0800000 29.99 35.11 29.0800000
## 1230 77 29.0800000 29.99 35.11 29.0800000
## 1231 77 29.0800000 29.99 35.11 29.0800000
## 1232 77 29.0800000 29.99 35.11 29.0800000
## 1233 77 29.0800000 29.99 35.11 29.0800000
## 1234 77 29.0800000 29.99 35.11 29.0800000
## 1235 77 29.0800000 29.99 35.11 29.0800000
## 1236 79 39.5800000 38.84 38.38 39.5800000
## 1237 79 39.5800000 38.84 38.38 39.5800000
## 1238 79 39.5800000 38.84 38.38 39.5800000
## 1239 79 39.5800000 38.84 38.38 39.5800000
## 1240 79 39.5800000 38.84 38.38 39.5800000
## 1241 79 39.5800000 38.84 38.38 39.5800000
## 1242 79 39.5800000 38.84 38.38 39.5800000
## 1243 79 39.5800000 38.84 38.38 39.5800000
## 1244 79 39.5800000 38.84 38.38 39.5800000
## 1245 79 39.5800000 38.84 38.38 39.5800000
## 1246 79 39.5800000 38.84 38.38 39.5800000
## 1247 79 39.5800000 38.84 38.38 39.5800000
## 1248 79 39.5800000 38.84 38.38 39.5800000
## 1249 8 17.5300000 17.04 17.35 17.5300000
## 1250 8 17.5300000 17.04 17.35 17.5300000
## 1251 8 17.5300000 17.04 17.35 17.5300000
## 1252 8 17.5300000 17.04 17.35 17.5300000
## 1253 8 17.5300000 17.04 17.35 17.5300000
## 1254 8 17.5300000 17.04 17.35 17.5300000
## 1255 8 17.5300000 17.04 17.35 17.5300000
## 1256 8 17.5300000 17.04 17.35 17.5300000
## 1257 8 17.5300000 17.04 17.35 17.5300000
## 1258 8 17.5300000 17.04 17.35 17.5300000
## 1259 8 17.5300000 17.04 17.35 17.5300000
## 1260 8 17.5300000 17.04 17.35 17.5300000
## 1261 8 17.5300000 17.04 17.35 17.5300000
## 1262 80 60.8500000 60.29 62.05 60.8500000
## 1263 80 60.8500000 60.29 62.05 60.8500000
## 1264 80 60.8500000 60.29 62.05 60.8500000
## 1265 80 60.8500000 60.29 62.05 60.8500000
## 1266 80 60.8500000 60.29 62.05 60.8500000
## 1267 80 60.8500000 60.29 62.05 60.8500000
## 1268 80 60.8500000 60.29 62.05 60.8500000
## 1269 80 60.8500000 60.29 62.05 60.8500000
## 1270 80 60.8500000 60.29 62.05 60.8500000
## 1271 80 60.8500000 60.29 62.05 60.8500000
## 1272 80 60.8500000 60.29 62.05 60.8500000
## 1273 80 60.8500000 60.29 62.05 60.8500000
## 1274 80 60.8500000 60.29 62.05 60.8500000
## 1275 81 63.9100000 64.62 67.52 63.9100000
## 1276 81 63.9100000 64.62 67.52 63.9100000
## 1277 81 63.9100000 64.62 67.52 63.9100000
## 1278 81 63.9100000 64.62 67.52 63.9100000
## 1279 81 63.9100000 64.62 67.52 63.9100000
## 1280 81 63.9100000 64.62 67.52 63.9100000
## 1281 81 63.9100000 64.62 67.52 63.9100000
## 1282 81 63.9100000 64.62 67.52 63.9100000
## 1283 81 63.9100000 64.62 67.52 63.9100000
## 1284 81 63.9100000 64.62 67.52 63.9100000
## 1285 81 63.9100000 64.62 67.52 63.9100000
## 1286 81 63.9100000 64.62 67.52 63.9100000
## 1287 81 63.9100000 64.62 67.52 63.9100000
## 1288 82 75.8200000 74.19 76.76 75.8200000
## 1289 82 75.8200000 74.19 76.76 75.8200000
## 1290 82 75.8200000 74.19 76.76 75.8200000
## 1291 82 75.8200000 74.19 76.76 75.8200000
## 1292 82 75.8200000 74.19 76.76 75.8200000
## 1293 82 75.8200000 74.19 76.76 75.8200000
## 1294 82 75.8200000 74.19 76.76 75.8200000
## 1295 82 75.8200000 74.19 76.76 75.8200000
## 1296 82 75.8200000 74.19 76.76 75.8200000
## 1297 82 75.8200000 74.19 76.76 75.8200000
## 1298 82 75.8200000 74.19 76.76 75.8200000
## 1299 82 75.8200000 74.19 76.76 75.8200000
## 1300 82 75.8200000 74.19 76.76 75.8200000
## 1301 83 3.8000000 3.97 1.00 3.8000000
## 1302 83 3.8000000 3.97 1.00 3.8000000
## 1303 83 3.8000000 3.97 1.00 3.8000000
## 1304 83 3.8000000 3.97 1.00 3.8000000
## 1305 83 3.8000000 3.97 1.00 3.8000000
## 1306 83 3.8000000 3.97 1.00 3.8000000
## 1307 83 3.8000000 3.97 1.00 3.8000000
## 1308 83 3.8000000 3.97 1.00 3.8000000
## 1309 83 3.8000000 3.97 1.00 3.8000000
## 1310 83 3.8000000 3.97 1.00 3.8000000
## 1311 83 3.8000000 3.97 1.00 3.8000000
## 1312 83 3.8000000 3.97 1.00 3.8000000
## 1313 83 3.8000000 3.97 1.00 3.8000000
## 1314 84 83.9900000 83.54 84.10 83.9900000
## 1315 84 83.9900000 83.54 84.10 83.9900000
## 1316 84 83.9900000 83.54 84.10 83.9900000
## 1317 84 83.9900000 83.54 84.10 83.9900000
## 1318 84 83.9900000 83.54 84.10 83.9900000
## 1319 84 83.9900000 83.54 84.10 83.9900000
## 1320 84 83.9900000 83.54 84.10 83.9900000
## 1321 84 83.9900000 83.54 84.10 83.9900000
## 1322 84 83.9900000 83.54 84.10 83.9900000
## 1323 84 83.9900000 83.54 84.10 83.9900000
## 1324 84 83.9900000 83.54 84.10 83.9900000
## 1325 84 83.9900000 83.54 84.10 83.9900000
## 1326 84 83.9900000 83.54 84.10 83.9900000
## 1327 86 66.3500000 66.98 68.92 66.3500000
## 1328 86 66.3500000 66.98 68.92 66.3500000
## 1329 86 66.3500000 66.98 68.92 66.3500000
## 1330 86 66.3500000 66.98 68.92 66.3500000
## 1331 86 66.3500000 66.98 68.92 66.3500000
## 1332 86 66.3500000 66.98 68.92 66.3500000
## 1333 86 66.3500000 66.98 68.92 66.3500000
## 1334 86 66.3500000 66.98 68.92 66.3500000
## 1335 86 66.3500000 66.98 68.92 66.3500000
## 1336 86 66.3500000 66.98 68.92 66.3500000
## 1337 86 66.3500000 66.98 68.92 66.3500000
## 1338 86 66.3500000 66.98 68.92 66.3500000
## 1339 86 66.3500000 66.98 68.92 66.3500000
## 1340 87 42.8400000 41.87 44.72 42.8400000
## 1341 87 42.8400000 41.87 44.72 42.8400000
## 1342 87 42.8400000 41.87 44.72 42.8400000
## 1343 87 42.8400000 41.87 44.72 42.8400000
## 1344 87 42.8400000 41.87 44.72 42.8400000
## 1345 87 42.8400000 41.87 44.72 42.8400000
## 1346 87 42.8400000 41.87 44.72 42.8400000
## 1347 87 42.8400000 41.87 44.72 42.8400000
## 1348 87 42.8400000 41.87 44.72 42.8400000
## 1349 87 42.8400000 41.87 44.72 42.8400000
## 1350 87 42.8400000 41.87 44.72 42.8400000
## 1351 87 42.8400000 41.87 44.72 42.8400000
## 1352 87 42.8400000 41.87 44.72 42.8400000
## 1353 88 59.0100000 58.44 57.79 59.0100000
## 1354 88 59.0100000 58.44 57.79 59.0100000
## 1355 88 59.0100000 58.44 57.79 59.0100000
## 1356 88 59.0100000 58.44 57.79 59.0100000
## 1357 88 59.0100000 58.44 57.79 59.0100000
## 1358 88 59.0100000 58.44 57.79 59.0100000
## 1359 88 59.0100000 58.44 57.79 59.0100000
## 1360 88 59.0100000 58.44 57.79 59.0100000
## 1361 88 59.0100000 58.44 57.79 59.0100000
## 1362 88 59.0100000 58.44 57.79 59.0100000
## 1363 88 59.0100000 58.44 57.79 59.0100000
## 1364 88 59.0100000 58.44 57.79 59.0100000
## 1365 88 59.0100000 58.44 57.79 59.0100000
# length(unique(data_xy$sev))
summary(data_xy$sev)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.42 8.50 19.07 25.20 37.83 83.99
hist_sev_xy = data_xy %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean) %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "black", bins = 20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"))
EX.L1<-stack(paste("./pics/01-Xylella-tobacco-bg-white/","75.jpg",sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
## [1] "3 layers available"
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
## [1] "3 layers available"
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=200, cropAbove=T, plot = F)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
## [1] "3 layers available"
# plot(EX.L4$HUE)
rgb_fig_xy = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"))
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
gli_fig_xy = as.data.frame(EX.L4$HUE, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = HUE))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))
# rgb_fig_xy+ gli_fig_xy
# rgb_fig_sbr + gli_fig_sbr + hist_sev_sbr #+
# rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB+
rgb_fig_xy + gli_fig_xy +hist_sev_xy
## Warning: Removed 2 rows containing missing values (geom_bar).
# plot_layout(widths = c(1, 1, 1),
# heights = c(1,1,1))
#
# ggsave("figs/leaf_gli.png",dpi = 600, height = 7, width =8)
rgb_gg_xy = data_xy %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg_xy
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg_xy = data_xy %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg_xy
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg_xy,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg_xy,
nrow = 2,
rel_heights = c(0.5,1))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_XY.png", dpi = 500, height = 8, width = 10)
cor_xy = data_xy %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev, method = "spearman")$estimate,3),
P_value = round(cor.test(mean,sev, method = "spearman")$p.value,4)) %>%
arrange(-cor)
## `summarise()` ungrouping output (override with `.groups` argument)
cor_xy
## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 HUE 0.694 0
## 2 HI 0.627 0
## 3 SCI 0.623 0
## 4 SI 0.356 0.0002
## 5 Red 0.286 0.0032
## 6 BI 0.219 0.0252
## 7 BGI 0.214 0.0288
## 8 Blue 0.195 0.0463
## 9 gray 0.194 0.0477
## 10 Green 0.121 0.217
## 11 GLI -0.49 0
## 12 NGRDI -0.623 0
## 13 VARI -0.624 0
all_data_spread = data_xy %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean)
all_data_spread
## # A tibble: 105 x 15
## file sev BGI BI Blue GLI gray Green HI HUE NGRDI Red
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 26.8 0.544 167. 106. 0.139 182. 193. 0.868 -1.23 0.0151 187.
## 2 10 24.3 0.523 162. 99.4 0.149 176. 189. 0.829 -1.31 0.0208 181.
## 3 11 16.3 0.477 135. 78.5 0.184 148. 163. 0.632 -1.57 0.0508 147.
## 4 12 14.8 0.547 163. 103. 0.138 177. 188. 0.863 -1.18 0.0158 182.
## 5 126 7.92 0.640 188. 135. 0.100 201. 210. 0.995 -0.634 0.00123 210.
## 6 127 9.48 0.582 177. 118. 0.125 191. 202. 0.900 -1.12 0.0124 198.
## 7 128 9.39 0.509 145. 87.0 0.157 158. 170. 0.794 -1.47 0.0260 161.
## 8 129 0.49 0.514 121. 76.6 0.192 133. 148. 0.360 -1.57 0.0845 125.
## 9 13 13.6 0.488 129. 77.1 0.183 143. 156. 0.599 -1.56 0.0546 140.
## 10 130 47.8 0.603 176. 118. 0.102 189. 196. 1.15 0.122 -0.0137 201.
## # ... with 95 more rows, and 3 more variables: SCI <dbl>, SI <dbl>, VARI <dbl>
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)
## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 231.
## There were 13 predictors of which 13 had non-zero influence.
sqrt(min(gbm.fit$cv.error))
## [1] 12.43899
gbm.perf(gbm.fit, method = "cv")
## [1] 231
# find index for n trees with minimum CV error
min_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])
## [1] 12.43899
# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )
## Using 231 trees...
sqrt(mean(((pred)-all_data_spread$sev[-train])^2))
## [1] 11.5175
CCC((pred), all_data_spread$sev[-train])$rho.c$est
## [1] 0.6909319
plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)
## [1] 192
# randomize data
set.seed(123)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.80*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray +Red+Green+Blue + SI + SCI, #<<<<<
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC(pred, all_data_spread$sev[-train])$rho.c$est#<<<<<
}
## Using 4918 trees...
## Using 506 trees...
## Using 53 trees...
## Using 14 trees...
## Using 2081 trees...
## Using 191 trees...
## Using 18 trees...
## Using 13 trees...
## Using 2081 trees...
## Using 191 trees...
## Using 19 trees...
## Using 9 trees...
## Using 2081 trees...
## Using 191 trees...
## Using 19 trees...
## Using 9 trees...
## Using 4991 trees...
## Using 680 trees...
## Using 53 trees...
## Using 58 trees...
## Using 4974 trees...
## Using 530 trees...
## Using 53 trees...
## Using 60 trees...
## Using 4974 trees...
## Using 530 trees...
## Using 53 trees...
## Using 60 trees...
## Using 4974 trees...
## Using 530 trees...
## Using 53 trees...
## Using 60 trees...
## Using 4986 trees...
## Using 658 trees...
## Using 89 trees...
## Using 135 trees...
## Using 4986 trees...
## Using 658 trees...
## Using 89 trees...
## Using 135 trees...
## Using 4986 trees...
## Using 658 trees...
## Using 89 trees...
## Using 135 trees...
## Using 4986 trees...
## Using 658 trees...
## Using 89 trees...
## Using 135 trees...
## Using 4990 trees...
## Using 966 trees...
## Using 64 trees...
## Using 10 trees...
## Using 1956 trees...
## Using 170 trees...
## Using 19 trees...
##
## Using 19 trees...
## Using 1646 trees...
## Using 166 trees...
## Using 16 trees...
## Using 4662 trees...
## Using 1585 trees...
## Using 166 trees...
## Using 16 trees...
## Using 25 trees...
## Using 4748 trees...
## Using 498 trees...
## Using 54 trees...
## Using 16 trees...
## Using 2691 trees...
## Using 279 trees...
## Using 35 trees...
## Using 9 trees...
## Using 2691 trees...
## Using 279 trees...
## Using 35 trees...
## Using 9 trees...
## Using 2691 trees...
## Using 279 trees...
## Using 35 trees...
## Using 9 trees...
## Using 4987 trees...
## Using 927 trees...
## Using 96 trees...
## Using 26 trees...
## Using 4987 trees...
## Using 927 trees...
## Using 96 trees...
## Using 26 trees...
## Using 4987 trees...
## Using 927 trees...
## Using 96 trees...
## Using 26 trees...
## Using 4987 trees...
## Using 927 trees...
## Using 96 trees...
## Using 26 trees...
## Using 4969 trees...
## Using 928 trees...
## Using 52 trees...
## Using 23 trees...
## Using 1581 trees...
## Using 152 trees...
## Using 4993 trees...
## Using 5 trees...
## Using 1333 trees...
## Using 96 trees...
## Using 11 trees...
## Using 4 trees...
## Using 1312 trees...
## Using 94 trees...
## Using 11 trees...
## Using 4 trees...
## Using 4404 trees...
## Using 380 trees...
## Using 35 trees...
## Using 13 trees...
## Using 2265 trees...
## Using 207 trees...
## Using 25 trees...
## Using 4 trees...
## Using 2265 trees...
## Using 207 trees...
## Using 28 trees...
## Using 4 trees...
## Using 2265 trees...
## Using 207 trees...
## Using 28 trees...
## Using 4 trees...
## Using 4998 trees...
## Using 863 trees...
## Using 55 trees...
## Using 30 trees...
## Using 4608 trees...
## Using 429 trees...
## Using 56 trees...
## Using 18 trees...
## Using 4608 trees...
## Using 429 trees...
## Using 56 trees...
## Using 18 trees...
## Using 4608 trees...
## Using 429 trees...
## Using 56 trees...
## Using 18 trees...
## Using 5000 trees...
## Using 2091 trees...
## Using 218 trees...
## Using 59 trees...
## Using 2167 trees...
## Using 203 trees...
## Using 18 trees...
## Using 3 trees...
## Using 894 trees...
## Using 88 trees...
## Using 8 trees...
## Using 2 trees...
## Using 772 trees...
## Using 74 trees...
## Using 9 trees...
## Using 2 trees...
## Using 5000 trees...
## Using 630 trees...
## Using 63 trees...
## Using 13 trees...
## Using 2458 trees...
## Using 250 trees...
## Using 23 trees...
## Using 6 trees...
## Using 2392 trees...
## Using 235 trees...
## Using 21 trees...
## Using 7 trees...
## Using 2392 trees...
## Using 235 trees...
## Using 21 trees...
## Using 7 trees...
## Using 5000 trees...
## Using 719 trees...
## Using 76 trees...
## Using 29 trees...
## Using 3351 trees...
## Using 331 trees...
## Using 27 trees...
## Using 6 trees...
## Using 3351 trees...
## Using 331 trees...
## Using 27 trees...
## Using 6 trees...
## Using 3351 trees...
## Using 331 trees...
## Using 27 trees...
## Using 6 trees...
best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par
## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.30 3 5 0.65 19
## 2 0.30 5 5 0.65 4662
## 3 0.01 1 5 0.65 966
## 4 0.30 6 5 0.65 25
## 5 0.01 1 5 1.00 2091
## 6 0.01 1 5 0.80 928
## 7 0.30 1 5 0.80 23
## 8 0.30 1 5 1.00 59
## 9 0.10 1 5 1.00 218
## 10 0.10 1 5 0.65 64
## min_RMSE CCC
## 1 11.37018 0.8649715
## 2 12.21829 0.8407627
## 3 12.00979 0.8362572
## 4 12.57791 0.8356177
## 5 12.73349 0.8252694
## 6 12.61867 0.8244094
## 7 12.65166 0.8236509
## 8 12.65081 0.8226995
## 9 12.83123 0.8215836
## 10 12.07186 0.8157707
# gbm.tune$fit
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = (sev) ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+Red+Green+Blue+gray+SI+SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
par(mar = c(5, 8, 1, 1))
summary_gbm_xy = summary(
gbm.fit.final,
cBars = 15,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_xy = summary_gbm_xy %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_xy
# ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)
Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "HUE", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( HUE,(yhat)))+
geom_line()
LIME
library(lime)
model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}
# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
# predict values for test data
pred <- predict(gbm.fit.final,
n.trees = gbm.fit.final$n.trees,
all_data_spread[-train,])
# results
caret::RMSE((pred), all_data_spread[-train,]$sev)
## [1] 10.55157
CCC((pred), all_data_spread$sev[-train])$rho.c$est
## [1] 0.8649715
cor((pred), all_data_spread$sev[-train])^2
## [1] 0.757813
accuracy_xy =data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_xy
## RMSE r s.shift l.shift C.b CCC CIS
## 1 10.55157 0.8705245 1.077005 0.08563874 0.9936211 0.8649715 0.7, 0.94
conc_xy = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual,predict))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)
## `geom_smooth()` using formula 'y ~ x'
pics<-list.files("./pics/01-potato_late_bligh")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-potato_late_bligh/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=4)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
# EX1.Indices<- fieldIndex(mosaic = EX.L1,
# index = c("NGRDI","BGI","GLI", "SCI","HI", "SI"),
# myIndex = c("(Red-Blue)/Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex ="Red", cropValue=1, cropAbove=F, plot = F)
cut = mask(EX.L1, EX.L2$mask)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_PLB.txt")
box = read.table("data/indexes_PLB.txt")
library(gsheet)
sev_data = gsheet2tbl("https://docs.google.com/spreadsheets/d/1QMYhoFU4V4ItkAXyM_qYW4ODt0MctX9UJeFckB8bMQc/edit?usp=sharing")
## Warning: Missing column names filled in: 'X9' [9]
#
# sev_data %>%
# group_by(photo) %>%
# summarise(n())
length(unique(sev_data$photo))
## [1] 200
all_data_PI = box %>%
mutate(file = leaf) %>%
dplyr::select(-leaf) %>%
right_join(sev_data) #%>%
## Joining, by = "file"
# filter(sev>0) %>%
# mutate(sev = case_when(sev==0 ~0.001,
# sev >0 ~sev))
summary(all_data_PI$sev)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 37.24 49.47 47.63 63.78 92.38
hist_sev_pi = all_data_PI %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean) %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "black", bins =20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"),
axis.title.y = element_text(size=8))
EX.L1<-stack(paste("./pics/01-potato_late_bligh/","PI21_2D.png",sep = ""))
EX.L1<-aggregate(EX.L1, fact=10)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
## [1] "3 layers available"
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
## [1] "3 layers available"
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Red"), cropValue=1, cropAbove=F, plot = F)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot = F)
## [1] "3 layers available"
# plot(EX.L4$HUE)
rgb_fig_pi = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
xlim(250,1500)+
ylim(50,1300)+
theme(panel.background = element_rect(color = "white"))
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
gli_fig_pi = as.data.frame(EX.L4$BGI, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = BGI))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
xlim(250,1500)+
ylim(50,1300)+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))
# rgb_fig_pi + gli_fig_pi
rgb_fig_sbr + gli_fig_sbr + hist_sev_sbr #+
## Warning: Removed 2 rows containing missing values (geom_bar).
# rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB+
# rgb_fig_xy + gli_fig_xy +hist_sev_xy+
# rgb_fig_pi + gli_fig_pi +hist_sev_pi+
# plot_layout(widths = c(1, 1, 1),
# heights = c(1,1,1,1))+
# plot_annotation(tag_levels = 'A')
# ggsave("figs/leaf_gli.png",dpi = 600, height = 8, width =8)
rgb_gg_pi = all_data_PI %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg_pi
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg_pi = all_data_PI %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg_pi
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg_pi,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg_pi,
nrow = 2,
rel_heights = c(0.5,1))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_pi.png", dpi = 500, height = 8, width = 10)
cor_PI = all_data_PI %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev, method = "spearman")$estimate,3),
P_value = round(cor.test(mean,sev, method = "spearman")$p.value,4)) %>%
arrange(-cor)
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev, method = "spearman"): Cannot compute
## exact p-value with ties
## `summarise()` ungrouping output (override with `.groups` argument)
cor_PI
## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 BGI 0.762 0
## 2 Blue 0.708 0
## 3 BI 0.399 0
## 4 gray 0.356 0
## 5 Red 0.295 0
## 6 Green 0.267 0.0001
## 7 SCI 0.106 0.136
## 8 HUE 0.098 0.169
## 9 VARI -0.069 0.332
## 10 NGRDI -0.106 0.136
## 11 HI -0.161 0.0224
## 12 GLI -0.492 0
## 13 SI -0.724 0
all_data_spread = all_data_PI %>%
pivot_wider(id_col = c(file,sev),
names_from = index,
values_from = mean)
all_data_spread
## # A tibble: 200 x 15
## file sev BGI BI Blue GLI gray Green HI HUE NGRDI Red
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 foto~ 13.6 0.254 57.1 20.7 0.304 64.0 74.1 0.511 -0.663 0.0975 60.9
## 2 foto~ 11.8 0.227 54.3 18.0 0.311 60.8 70.3 0.550 -0.682 0.0919 58.6
## 3 foto~ 0.95 0.0561 45.4 4.38 0.477 51.6 64.9 0.282 -0.658 0.209 43.4
## 4 foto~ 15.2 0.313 60.2 26.0 0.274 67.4 77.2 0.499 -0.665 0.0914 64.0
## 5 foto~ 7.81 0.289 55.1 25.5 0.391 62.3 76.5 -0.194 -0.853 0.248 48.4
## 6 foto~ 1.39 0.360 56.3 28.6 0.287 63.0 73.5 0.183 -0.783 0.138 55.5
## 7 foto~ 2.08 0.318 52.9 25.0 0.313 59.2 69.6 0.146 -0.686 0.148 51.9
## 8 foto~ 2.03 0.204 52.9 16.2 0.338 59.7 70.4 0.499 -0.660 0.118 55.3
## 9 foto~ 0.6 0.199 51.7 15.5 0.336 58.2 68.3 0.507 -0.682 0.111 54.5
## 10 foto~ 1.74 0.281 49.6 20.6 0.299 55.7 64.7 0.446 -0.620 0.110 51.4
## # ... with 190 more rows, and 3 more variables: SCI <dbl>, SI <dbl>, VARI <dbl>
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)
## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 80.
## There were 13 predictors of which 13 had non-zero influence.
sqrt(min(gbm.fit$cv.error))
## [1] 14.0629
gbm.perf(gbm.fit, method = "cv")
## [1] 80
# find index for n trees with minimum CV error
min_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])
## [1] 14.0629
# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )
## Using 80 trees...
sqrt(mean(((pred)-all_data_spread$sev[-train])^2))
## [1] 13.84988
CCC((pred), all_data_spread$sev[-train])$rho.c$est
## [1] 0.7509533
plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)
## [1] 192
# randomize data
set.seed(123)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.80*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC((pred), all_data_spread$sev[-train])$rho.c$est
}
## Using 4982 trees...
## Using 936 trees...
## Using 290 trees...
## Using 18 trees...
## Using 2198 trees...
## Using 215 trees...
## Using 26 trees...
## Using 6 trees...
## Using 2231 trees...
## Using 296 trees...
## Using 25 trees...
## Using 6 trees...
## Using 2231 trees...
## Using 329 trees...
## Using 30 trees...
## Using 6 trees...
## Using 4669 trees...
## Using 496 trees...
## Using 42 trees...
## Using 269 trees...
## Using 3338 trees...
## Using 307 trees...
## Using 30 trees...
## Using 9 trees...
## Using 3338 trees...
## Using 343 trees...
## Using 25 trees...
##
## Using 25 trees...
## Using 3338 trees...
## Using 343 trees...
## Using 25 trees...
##
## Using 25 trees...
## Using 4617 trees...
## Using 496 trees...
## Using 47 trees...
## Using 24 trees...
## Using 4601 trees...
## Using 3363 trees...
## Using 52 trees...
## Using 147 trees...
## Using 4601 trees...
## Using 3363 trees...
## Using 52 trees...
## Using 147 trees...
## Using 4601 trees...
## Using 3363 trees...
## Using 52 trees...
## Using 147 trees...
## Using 4991 trees...
## Using 936 trees...
## Using 83 trees...
## Using 23 trees...
## Using 2283 trees...
## Using 275 trees...
## Using 29 trees...
## Using 6 trees...
## Using 2181 trees...
## Using 206 trees...
## Using 23 trees...
## Using 6 trees...
## Using 2200 trees...
## Using 210 trees...
## Using 23 trees...
## Using 6 trees...
## Using 4996 trees...
## Using 725 trees...
## Using 82 trees...
## Using 50 trees...
## Using 2792 trees...
## Using 269 trees...
## Using 21 trees...
## Using 9 trees...
## Using 2781 trees...
## Using 298 trees...
## Using 74 trees...
## Using 51 trees...
## Using 2781 trees...
## Using 277 trees...
## Using 21 trees...
## Using 9 trees...
## Using 4661 trees...
## Using 495 trees...
## Using 50 trees...
## Using 10 trees...
## Using 4437 trees...
## Using 367 trees...
## Using 50 trees...
## Using 10 trees...
## Using 4246 trees...
## Using 356 trees...
## Using 52 trees...
## Using 10 trees...
## Using 4246 trees...
## Using 356 trees...
## Using 52 trees...
## Using 10 trees...
## Using 4995 trees...
## Using 954 trees...
## Using 59 trees...
## Using 32 trees...
## Using 2542 trees...
## Using 264 trees...
## Using 22 trees...
## Using 7 trees...
## Using 2186 trees...
## Using 214 trees...
## Using 22 trees...
## Using 7 trees...
## Using 2047 trees...
## Using 250 trees...
## Using 22 trees...
## Using 5 trees...
## Using 4995 trees...
## Using 1195 trees...
## Using 251 trees...
## Using 32 trees...
## Using 2467 trees...
## Using 285 trees...
## Using 22 trees...
## Using 6 trees...
## Using 2663 trees...
## Using 238 trees...
## Using 22 trees...
## Using 6 trees...
## Using 2612 trees...
## Using 214 trees...
## Using 25 trees...
## Using 7 trees...
## Using 4995 trees...
## Using 689 trees...
## Using 71 trees...
## Using 22 trees...
## Using 3167 trees...
## Using 285 trees...
## Using 36 trees...
## Using 10 trees...
## Using 3846 trees...
## Using 344 trees...
## Using 35 trees...
## Using 9 trees...
## Using 3846 trees...
## Using 344 trees...
## Using 35 trees...
## Using 9 trees...
## Using 4006 trees...
## Using 3446 trees...
## Using 327 trees...
## Using 72 trees...
## Using 2391 trees...
## Using 237 trees...
## Using 31 trees...
## Using 7 trees...
## Using 2297 trees...
## Using 232 trees...
## Using 19 trees...
## Using 7 trees...
## Using 2519 trees...
## Using 219 trees...
## Using 26 trees...
## Using 6 trees...
## Using 5000 trees...
## Using 1416 trees...
## Using 139 trees...
## Using 31 trees...
## Using 3049 trees...
## Using 329 trees...
## Using 37 trees...
## Using 11 trees...
## Using 2371 trees...
## Using 236 trees...
## Using 24 trees...
## Using 7 trees...
## Using 2302 trees...
## Using 232 trees...
## Using 21 trees...
## Using 7 trees...
## Using 4999 trees...
## Using 3942 trees...
## Using 371 trees...
## Using 138 trees...
## Using 3041 trees...
## Using 312 trees...
## Using 23 trees...
## Using 8 trees...
## Using 3754 trees...
## Using 348 trees...
## Using 35 trees...
## Using 9 trees...
## Using 4014 trees...
## Using 405 trees...
## Using 35 trees...
## Using 10 trees...
best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par
## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.300 1 15 0.50 24
## 2 0.300 3 15 0.65 10
## 3 0.300 5 15 0.65 10
## 4 0.300 6 15 0.65 10
## 5 0.300 1 15 0.80 22
## 6 0.300 1 15 1.00 138
## 7 0.010 1 15 0.65 495
## 8 0.010 1 15 0.80 689
## 9 0.001 1 15 0.65 4661
## 10 0.001 1 15 0.50 4617
## min_RMSE CCC
## 1 12.22198 0.7088796
## 2 11.82089 0.7009918
## 3 12.21880 0.7008317
## 4 12.21880 0.7008317
## 5 11.57058 0.6999291
## 6 12.01776 0.6954443
## 7 12.60668 0.6931396
## 8 12.49874 0.6891423
## 9 12.58996 0.6836161
## 10 12.76863 0.6821743
# gbm.tune$fit
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
par(mar = c(5, 8, 1, 1))
summary_gbm_plb = summary(
gbm.fit.final,
cBars = 13,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_plb = summary_gbm_plb %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_plb
# ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)
Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "BGI", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( BGI,(yhat)))+
geom_line()
LIME
library(lime)
model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}
# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
# predict values for test data
pred <- predict(gbm.fit.final, n.trees = gbm.fit.final$n.trees, all_data_spread[-train,])
# results
caret::RMSE(pred, all_data_spread[-train,]$sev)
## [1] 13.38866
CCC(pred, all_data_spread$sev[-train])$rho.c$est
## [1] 0.7088796
cor(pred, all_data_spread$sev[-train])^2
## [1] 0.5509299
accuracy_pi =data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_pi
## RMSE r s.shift l.shift C.b CCC CIS
## 1 13.38866 0.7422465 1.285102 -0.1757541 0.9550461 0.7088796 0.53, 0.83
conc_pi = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual,predict))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)
## `geom_smooth()` using formula 'y ~ x'
pics<-list.files("./pics/01-Calonectria_leaf_bligth")
# length(pics)
#indices
index = c("BI","SCI","GLI","HI","SI","VARI","HUE","BGI","NGRDI")
box = data.frame()
for(i in 1:length(pics)){
EX.L1<-stack(paste("./pics/01-Calonectria_leaf_bligth/",pics[i],sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
EX1.Indices<- fieldIndex(mosaic = EX.L1,
index = index,
myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=175, cropAbove=T, plot = F)
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
df = as(EX.L4, "SpatialPixelsDataFrame")
dff = as.data.frame(df) %>%
mutate(gray = 0.299*Red+0.587*Green+0.114*Blue) %>%
gather(c(1:(3+length(index)),15), key = "index", value = "value" ) %>%
filter(!is.na(value),
!is.infinite(value)) %>%
group_by(index) %>%
dplyr::summarise(mean = mean(value, na.rm = T),
std = sd(value),
Q25 = quantile(value,0.25),
Q50 = quantile(value,0.50),
Q75 = quantile(value,0.75)) %>%
mutate(leaf = pics[i])
box = box %>%
bind_rows(dff)}
length(unique(box$leaf))
write.table(box,"data/indexes_calonec.txt")
box = read.table("data/indexes_calonec.txt")
data_calo_load = gsheet2tbl("https://docs.google.com/spreadsheets/d/1Nqd9NqbynjR1bI2wrYdsKXZT3HACT8CVqyfY5n70DhI/edit?usp=sharing") %>%
mutate(file = as.character(file)) %>%
dplyr::select(file,area_total,area_doente_roxa,area_doente,sev_roxa,sev)
head(data_calo_load)
## # A tibble: 6 x 6
## file area_total area_doente_roxa area_doente sev_roxa sev
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 411776 54374 9384 13.2 2.28
## 2 2 415627 81757 55557 19.7 13.4
## 3 3 761523 569173 413002 74.7 54.2
## 4 4 798787 708346 594451 88.7 74.4
## 5 5 657200 219361 85449 33.4 13.0
## 6 6 784817 628389 628389 80.1 80.1
data_calo = box %>%
separate(leaf, into=c("file","format"), sep =".jpg") %>%
dplyr::select(-format) %>%
full_join(data_calo_load, by="file") %>%
mutate(sev=sev_roxa)
# data_calo
length(unique(data_calo$sev))
## [1] 300
summary(data_calo$sev)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8715 4.4431 9.8264 16.2402 20.1989 95.4470
hist_sev_calo = data_calo %>%
pivot_wider(id_col = c(file,sev_roxa),
names_from = index,
values_from = mean) %>%
ggplot(aes(sev_roxa))+
geom_histogram(color = "white", fill = "black", bins = 20)+
theme_minimal_hgrid(font_size = 10)+
labs(x = "Severity (%)",
y = "Frequency")+
scale_x_continuous(limits = c(-5,105), breaks = seq(0,100,25))+
# theme_void()+
# coord_fixed()+
theme(panel.background = element_rect(color = "black"),
axis.title.y = element_text(size=8))
EX.L1<-stack(paste("./pics/01-Calonectria_leaf_bligth/","57.jpg",sep = ""))
EX.L1<-aggregate(EX.L1, fact=7)
EX.L.Shape<-fieldPolygon(mosaic=EX.L1, extent=T, plot = F)
## [1] "3 layers available"
# EX1.Indices<- fieldIndex(mosaic = EX.L1,
# index = index,
# myIndex = c("Green"), plot = F)
EX.L2<-fieldMask(mosaic=EX.L1, myIndex = c("Blue"), cropValue=175, cropAbove=T, plot = F)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
cut = mask(EX.L1, EX.L2$newMosaic)
EX.L4<-fieldIndex(mosaic=cut,
index =index,
plot =F)
## [1] "3 layers available"
# plot(EX.L4$HUE)
rgb_fig_calo = RStoolbox::ggRGB(EX.L2$newMosaic,
r = 1,
g = 2,
b = 3)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"))
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
gli_fig_calo = as.data.frame(EX.L4$HUE, xy=TRUE, na.rm =T) %>%
ggplot(aes(x, y, fill = HUE))+
geom_tile()+
scale_fill_viridis_c(option = "B",direction = -1)+
theme_map()+
coord_fixed()+
theme(panel.background = element_rect(color = "white"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8))
rgb_fig_calo+ gli_fig_calo
rgb_fig_sbr + gli_fig_sbr + hist_sev_sbr +
rgb_fig_xy + gli_fig_xy +hist_sev_xy+
rgb_fig_calo + gli_fig_calo + hist_sev_calo+
rgb_fig_wlb + gli_fig_wlb + hist_sev_WLB+
rgb_fig_pi + gli_fig_pi + hist_sev_pi+
plot_layout(widths = c(1, 1, 1),
heights = c(1,1,1,1,1))+
plot_annotation(tag_levels = 'A')&
theme(legend.key.size = unit(3, 'mm'),
legend.text = element_text(size =6))
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
ggsave("figs/leaf_gli.png",dpi = 600, height = 9, width =7)
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 2 rows containing missing values (geom_bar).
rgb_gg_calo = data_calo %>%
filter(index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev_roxa, color = index)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =1)+
scale_color_manual(values = c("steelblue","darkgreen", "darkred"))+
theme_minimal_hgrid()+
labs(x = "Mean value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))+
theme(legend.position = "none")
rgb_gg_calo
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
index_gg_calo = data_calo %>%
filter(!index %in% c("Red", "Blue", "Green")) %>%
ggplot(aes(mean, sev_roxa, label = file)) +
# geom_text()+
geom_point(color = "gray", size =3)+
geom_smooth(color = "black", se = F, size = 2)+
facet_wrap(~index, scales = "free_x", nrow =2)+
theme_minimal_hgrid()+
labs(x = "Mean index value in the image",
y = "Disease severity (%)")+
theme(panel.border = element_rect(color = "gray"))
index_gg_calo
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot_grid(
plot_grid(NULL,rgb_gg_calo,NULL, rel_widths =c(0.18,1,0.2), nrow = 1),
index_gg_calo,
nrow = 2,
rel_heights = c(0.5,1))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggsave("figs/index_sev_calo.png", dpi = 500, height = 8, width = 10)
cor_calo = data_calo %>%
group_by(index) %>%
dplyr::summarise(cor = round( cor.test(mean,sev_roxa, method = "spearman")$estimate,3),
P_value = round(cor.test(mean,sev_roxa, method = "spearman")$p.value,4)) %>%
arrange(-cor)
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## Warning in cor.test.default(mean, sev_roxa, method = "spearman"): Cannot compute
## exact p-value with ties
## `summarise()` ungrouping output (override with `.groups` argument)
cor_calo
## # A tibble: 13 x 3
## index cor P_value
## <chr> <dbl> <dbl>
## 1 HUE 0.982 0
## 2 SCI 0.773 0
## 3 Red 0.754 0
## 4 SI 0.678 0
## 5 HI 0.621 0
## 6 BI 0.568 0
## 7 gray 0.479 0
## 8 BGI 0.405 0
## 9 Blue 0.36 0
## 10 Green 0.065 0.264
## 11 GLI -0.736 0
## 12 VARI -0.743 0
## 13 NGRDI -0.773 0
all_data_spread = data_calo %>%
pivot_wider(id_col = c(file,sev_roxa),
names_from = index,
values_from = mean) %>%
mutate(sev = sev_roxa) %>%
dplyr::select(-sev_roxa)
all_data_spread
## # A tibble: 300 x 15
## file BGI BI Blue GLI gray Green HI HUE NGRDI Red
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.688 87.8 75.2 0.225 93.8 111. -0.985 -1.21 0.265 66.6
## 2 10 0.733 152. 109. 0.00994 155. 149. 2.75 1.30 -0.101 185.
## 3 100 0.678 95.3 82.5 0.231 102. 122. -1.04 -1.36 0.274 71.5
## 4 101 0.661 97.5 79.9 0.204 105. 122. -0.490 -1.27 0.203 82.7
## 5 102 0.631 87.3 71.9 0.257 94.6 114. -1.05 -1.35 0.291 65.1
## 6 103 0.599 90.4 69.1 0.243 98.7 117. -0.516 -1.23 0.233 74.2
## 7 104 0.573 89.2 69.9 0.307 97.8 122. -1.11 -1.45 0.344 61.3
## 8 105 0.768 92.9 77.9 0.0973 97.0 103. 1.29 -0.417 0.0721 92.9
## 9 106 0.645 102. 79.6 0.187 110. 124. 0.765 -1.01 0.161 93.1
## 10 107 0.660 88.9 76.0 0.241 96.1 115. -1.40 -1.45 0.279 66.6
## # ... with 290 more rows, and 4 more variables: SCI <dbl>, SI <dbl>,
## # VARI <dbl>, sev <dbl>
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.75*length(all_data_spread$sev),1))
# length(train)
gbm.fit = gbm(sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 3,
shrinkage = 0.1,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
print(gbm.fit)
## gbm(formula = sev ~ BGI + BI + GLI + HI + HUE + NGRDI + VARI +
## gray + Red + Green + Blue + SI + SCI, distribution = "gaussian",
## data = all_data_spread[train, ], n.trees = 1000, interaction.depth = 3,
## shrinkage = 0.1, cv.folds = 5, verbose = FALSE, n.cores = NULL)
## A gradient boosted model with gaussian loss function.
## 1000 iterations were performed.
## The best cross-validation iteration was 185.
## There were 13 predictors of which 13 had non-zero influence.
sqrt(min(gbm.fit$cv.error))
## [1] 4.651259
gbm.perf(gbm.fit, method = "cv")
## [1] 185
# find index for n trees with minimum CV error
min_MSE <- which.min(gbm.fit$cv.error)
sqrt(gbm.fit$cv.error[min_MSE])
## [1] 4.651259
# best.iter <- gbm.perf(model1, method = "test")
# print(best.iter)
pred = predict(gbm.fit, newdata = all_data_spread[-train,-1], ntrees = 5000 )
## Using 185 trees...
sqrt(mean(((pred)-all_data_spread$sev[-train])^2))
## [1] 4.170014
CCC((pred), all_data_spread$sev[-train])$rho.c$est
## [1] 0.9735623
plot((pred), (pred)-all_data_spread$sev[-train])
abline(a=0,b=0)
Create hyperparameter grid
hyper_grid <- expand.grid(
shrinkage = c(.001, .01, .1, .3),
interaction.depth = c(1, 3, 5, 6),
n.minobsinnode = c(5, 10, 15),
bag.fraction = c(.5,.65, .8, 1),
optimal_trees = 0, # a place to dump results
min_RMSE = 0,
CCC =0 # a place to dump results
)
# total number of combinations
nrow(hyper_grid)
## [1] 192
# randomize data
set.seed(1234)
train=sample(x = 1:length(all_data_spread$sev),
size = round(0.80*length(all_data_spread$sev),1))
# grid search
for(i in 1:nrow(hyper_grid)) {
# reproducibility
set.seed(123)
# train model
gbm.tune <- gbm(
formula = sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = 5000,
interaction.depth = hyper_grid$interaction.depth[i],
shrinkage = hyper_grid$shrinkage[i],
n.minobsinnode = hyper_grid$n.minobsinnode[i],
bag.fraction = hyper_grid$bag.fraction[i],
train.fraction = .75,
n.cores = NULL, # will use all cores by default
verbose = FALSE)
pred = predict(gbm.tune, newdata = all_data_spread[-train,-1], ntrees = 5000 )
# add min training error and trees to grid
hyper_grid$optimal_trees[i] <- which.min(gbm.tune$valid.error)
hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
hyper_grid$CCC[i] = CCC((pred), all_data_spread$sev[-train])$rho.c$est
}
## Using 5000 trees...
## Using 4835 trees...
## Using 555 trees...
## Using 104 trees...
## Using 5000 trees...
## Using 4997 trees...
## Using 1871 trees...
## Using 4957 trees...
## Using 5000 trees...
## Using 4957 trees...
## Using 1689 trees...
## Using 1683 trees...
## Using 5000 trees...
## Using 4987 trees...
## Using 1968 trees...
## Using 12 trees...
## Using 4996 trees...
## Using 5000 trees...
## Using 546 trees...
## Using 42 trees...
## Using 4996 trees...
## Using 1376 trees...
## Using 115 trees...
## Using 40 trees...
## Using 4996 trees...
## Using 1377 trees...
## Using 56 trees...
## Using 24 trees...
## Using 4996 trees...
## Using 1376 trees...
## Using 58 trees...
## Using 4114 trees...
## Using 4293 trees...
## Using 1642 trees...
## Using 201 trees...
## Using 81 trees...
## Using 4996 trees...
## Using 661 trees...
## Using 47 trees...
## Using 42 trees...
## Using 4996 trees...
## Using 863 trees...
## Using 47 trees...
## Using 31 trees...
## Using 4996 trees...
## Using 863 trees...
## Using 47 trees...
## Using 31 trees...
## Using 5000 trees...
## Using 4837 trees...
## Using 395 trees...
## Using 114 trees...
## Using 5000 trees...
## Using 2697 trees...
## Using 253 trees...
## Using 394 trees...
## Using 4998 trees...
## Using 2692 trees...
## Using 175 trees...
## Using 4982 trees...
## Using 4998 trees...
## Using 3721 trees...
## Using 456 trees...
## Using 244 trees...
## Using 5000 trees...
## Using 3831 trees...
## Using 461 trees...
## Using 97 trees...
## Using 5000 trees...
## Using 1073 trees...
## Using 115 trees...
## Using 4999 trees...
## Using 5000 trees...
## Using 712 trees...
## Using 115 trees...
## Using 35 trees...
## Using 5000 trees...
## Using 706 trees...
## Using 115 trees...
## Using 37 trees...
## Using 4974 trees...
## Using 1640 trees...
## Using 147 trees...
## Using 96 trees...
## Using 5000 trees...
## Using 822 trees...
## Using 46 trees...
## Using 30 trees...
## Using 5000 trees...
## Using 955 trees...
## Using 46 trees...
## Using 37 trees...
## Using 5000 trees...
## Using 955 trees...
## Using 46 trees...
## Using 30 trees...
## Using 5000 trees...
## Using 3047 trees...
## Using 379 trees...
## Using 62 trees...
## Using 4998 trees...
## Using 1315 trees...
## Using 253 trees...
## Using 105 trees...
## Using 4998 trees...
## Using 1611 trees...
## Using 132 trees...
## Using 26 trees...
## Using 4998 trees...
## Using 4957 trees...
## Using 128 trees...
## Using 26 trees...
## Using 5000 trees...
## Using 4828 trees...
## Using 1231 trees...
## Using 1735 trees...
## Using 5000 trees...
## Using 2842 trees...
## Using 3829 trees...
## Using 14 trees...
## Using 5000 trees...
## Using 525 trees...
## Using 79 trees...
## Using 29 trees...
## Using 5000 trees...
## Using 539 trees...
## Using 55 trees...
## Using 23 trees...
## Using 4999 trees...
## Using 4606 trees...
## Using 331 trees...
## Using 160 trees...
## Using 5000 trees...
## Using 1046 trees...
## Using 85 trees...
## Using 34 trees...
## Using 5000 trees...
## Using 870 trees...
## Using 89 trees...
## Using 38 trees...
## Using 5000 trees...
## Using 870 trees...
## Using 85 trees...
## Using 31 trees...
## Using 5000 trees...
## Using 4991 trees...
## Using 843 trees...
## Using 262 trees...
## Using 5000 trees...
## Using 3072 trees...
## Using 213 trees...
## Using 53 trees...
## Using 5000 trees...
## Using 1799 trees...
## Using 242 trees...
## Using 51 trees...
## Using 5000 trees...
## Using 2183 trees...
## Using 2385 trees...
## Using 44 trees...
## Using 5000 trees...
## Using 4104 trees...
## Using 582 trees...
## Using 1097 trees...
## Using 5000 trees...
## Using 588 trees...
## Using 63 trees...
## Using 11 trees...
## Using 4999 trees...
## Using 548 trees...
## Using 52 trees...
## Using 15 trees...
## Using 5000 trees...
## Using 584 trees...
## Using 58 trees...
## Using 14 trees...
## Using 5000 trees...
## Using 1121 trees...
## Using 133 trees...
## Using 156 trees...
## Using 3806 trees...
## Using 382 trees...
## Using 42 trees...
## Using 11 trees...
## Using 3959 trees...
## Using 400 trees...
## Using 41 trees...
## Using 12 trees...
## Using 3830 trees...
## Using 394 trees...
## Using 37 trees...
## Using 12 trees...
best_par = hyper_grid %>%
dplyr::arrange(-CCC) %>%
head(10)
best_par
## shrinkage interaction.depth n.minobsinnode bag.fraction optimal_trees
## 1 0.300 3 5 1 53
## 2 0.100 1 5 1 843
## 3 0.010 1 5 1 4991
## 4 0.300 1 5 1 262
## 5 0.100 3 5 1 213
## 6 0.001 5 5 1 5000
## 7 0.001 6 5 1 5000
## 8 0.300 6 5 1 44
## 9 0.300 5 5 1 51
## 10 0.001 3 5 1 5000
## min_RMSE CCC
## 1 3.188960 0.9921566
## 2 2.913543 0.9917062
## 3 3.008064 0.9912300
## 4 3.429057 0.9907054
## 5 2.960664 0.9906812
## 6 3.378191 0.9904229
## 7 3.403406 0.9903718
## 8 3.064615 0.9901287
## 9 3.177959 0.9900388
## 10 3.297901 0.9900336
# gbm.tune$fit
# for reproducibility
set.seed(123)
# train GBM model
gbm.fit.final <- gbm(
formula = sev ~BGI+BI+GLI+HI+HUE+NGRDI+VARI+gray+ Red+Green+Blue + SI + SCI,
data = all_data_spread[train,],
distribution = "gaussian",
n.trees = best_par$optimal_trees[1],
interaction.depth = best_par$interaction.depth[1],
shrinkage = best_par$shrinkage[1],
n.minobsinnode = best_par$n.minobsinnode[1],
bag.fraction = best_par$bag.fraction[1],
train.fraction =0.75,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
par(mar = c(5, 8, 1, 1))
summary_gbm_calo = summary(
gbm.fit.final,
cBars = 13,
method = relative.influence, # also can use permutation.test.gbm
las = 2
)
rel_calo = summary_gbm_calo %>%
rownames_to_column("index") %>%
ggplot(aes(rel.inf, reorder(var, rel.inf)))+
geom_col(aes(fill =rel.inf>1, color =rel.inf>1 ), width = 0.85)+
theme_minimal_vgrid()+
labs(x = "Relative influence (%)",
y = "Model predictors",
fill = "RI > 1%",
color = "RI > 1%")
rel_calo
# ggsave("figs/var_influence.png",dpi = 600, height = 4, width = 6)
Partial dependence plots
gbm.fit.final %>%
pdp::partial(pred.var = "HUE", n.trees = gbm.fit.final$n.trees, grid.resolution = 100) %>%
ggplot(aes( HUE,(yhat)))+
geom_line()
LIME
library(lime)
model_type.gbm <- function(x, ...) {
return("regression")
}
predict_model.gbm <- function(x, newdata, ...) {
pred <- predict(x, newdata, n.trees = x$n.trees)
return(as.data.frame(pred))
}
# get a few observations to perform local interpretation on
local_obs <- (all_data_spread[-train,])[1:4, ]
# apply LIME
explainer <- lime(all_data_spread[train,], gbm.fit.final)
explanation <- lime::explain(local_obs, explainer, n_features = 7, n.trees =1)
plot_features(explanation)
# predict values for test data
pred <- predict(gbm.fit.final, n.trees = gbm.fit.final$n.trees, all_data_spread[-train,])
# results
caret::RMSE(pred, all_data_spread[-train,]$sev)
## [1] 2.358233
CCC(pred, all_data_spread$sev[-train])$rho.c$est
## [1] 0.9921566
cor(pred, all_data_spread$sev[-train])^2
## [1] 0.9872815
accuracy_calo = data.frame(predi=pred, actual = all_data_spread$sev[-train]) %>%
summarise(RMSE = caret::RMSE(pred, actual),
r = cor(pred, actual),
s.shift = CCC(pred, actual)$s.shift,
l.shift = CCC(pred, actual)$l.shift,
C.b = CCC(pred, actual)$C.b,
CCC = CCC(pred, actual)$rho.c$est,
CIS = paste(
round(CCC(pred, all_data_spread$sev[-train])$rho.c[2],2),","," ",
round(CCC(pred, all_data_spread$sev[-train])$rho.c[3],2),sep = ""
))
accuracy_calo
## RMSE r s.shift l.shift C.b CCC CIS
## 1 2.358233 0.9936204 1.047502 0.02822577 0.9985268 0.9921566 0.99, 1
conc_calo = data.frame(predict = pred, actual =all_data_spread$sev[-train]) %>%
ggplot(aes(actual,predict))+
geom_point(size =2, color = "gray")+
geom_abline(intercept = 0, slope= 1, size = .81, color = "black", linetype = "dashed")+
geom_smooth(method = "lm",
color = "red",
size =.81, se =F,
fullrange=T)+
theme_minimal_grid()+
labs(x = "Predicted Severity (%)",
y = "Actual Severity (%)")+
coord_equal(xlim = c(0,100),
ylim = c(0,100))+
xlim(0,100)
ggsave("figs/concordance.png", dpi = 600, height = 3.5, width = 4)
## `geom_smooth()` using formula 'y ~ x'
ind_order = c("Red","Green","Blue","BI","SCI","GLI","HI","NGRDI","SI","VARI","HUE","BGI","gray")
bind_rows(
cor_sbr %>% mutate(disease="SBR"),
cor_calo %>% mutate(disease="CLB"),
cor_xy %>% mutate(disease="NtXf"),
cor_wlb %>% mutate(disease="WLB"),
cor_PI %>% mutate(disease="PLB")) %>%
mutate(sig = case_when(P_value <0.05 ~ " ",
P_value >0.05 ~ "P>0.05")) %>%
mutate(disease = factor(disease, levels = c("PLB", "WLB","NtXf","CLB", "SBR")),
index = factor(index, levels = ind_order)) %>%
ggplot(aes(index,disease, fill = cor, label = round(cor,3)))+
geom_tile()+
geom_text(size =3.5)+
geom_point(aes(index,disease, color = sig), shape = "X", size =8, alpha = 0.6)+
# scale_fill_gradient2(low = "darkred", mid = NA, high = "darkgreen")+
scale_fill_distiller(palette = "RdBu", direction = 1)+
scale_color_manual(values = c(NA,"black"))+
theme_half_open()+
labs(x = "",
y = "",
fill = " r",
color ="")
## Warning: Removed 59 rows containing missing values (geom_point).
ggsave("figs/corr.png",dpi = 600, height = 3.5, width =10)
## Warning: Removed 59 rows containing missing values (geom_point).
summary_gbm_sbr
## var rel.inf
## HUE HUE 87.88801884
## NGRDI NGRDI 6.28297306
## SCI SCI 4.37465525
## GLI GLI 0.47741094
## HI HI 0.39728015
## VARI VARI 0.15791219
## Red Red 0.15652889
## BI BI 0.11433955
## Blue Blue 0.06497336
## Green Green 0.03215579
## SI SI 0.02229384
## BGI BGI 0.02060409
## gray gray 0.01085406
summary_gbm_calo
## var rel.inf
## HUE HUE 94.03402438
## SCI SCI 4.09247026
## BI BI 0.81359184
## BGI BGI 0.28727216
## SI SI 0.18018170
## NGRDI NGRDI 0.14039540
## Blue Blue 0.13733611
## HI HI 0.09923759
## gray gray 0.06551079
## Green Green 0.05420577
## GLI GLI 0.04420588
## Red Red 0.03073479
## VARI VARI 0.02083332
summary_gbm_xy
## var rel.inf
## HUE HUE 45.0933820
## HI HI 19.2008467
## SI SI 16.0327582
## BGI BGI 7.1783997
## Green Green 5.0459348
## NGRDI NGRDI 3.7484209
## BI BI 2.1717994
## GLI GLI 0.7792653
## Red Red 0.7491929
## VARI VARI 0.0000000
## Blue Blue 0.0000000
## gray gray 0.0000000
## SCI SCI 0.0000000
summary_gbm_wlb
## var rel.inf
## VARI VARI 28.4651011
## HI HI 22.7166812
## Blue Blue 17.7541396
## GLI GLI 10.8559511
## SCI SCI 6.9322339
## HUE HUE 4.4167122
## NGRDI NGRDI 4.1782350
## BGI BGI 1.4914608
## SI SI 1.3595593
## Red Red 0.8233217
## BI BI 0.4903142
## Green Green 0.2792345
## gray gray 0.2370553
summary_gbm_plb
## var rel.inf
## SI SI 34.172257
## BGI BGI 23.132425
## HUE HUE 12.898477
## Green Green 9.907507
## Blue Blue 9.040377
## BI BI 3.744766
## GLI GLI 2.959053
## HI HI 2.437804
## gray gray 1.707335
## NGRDI NGRDI 0.000000
## VARI VARI 0.000000
## Red Red 0.000000
## SCI SCI 0.000000
rel_sbr + labs(title = paste("Soybean rust"))+
rel_calo +labs(title = paste("Calonectria leaf blight"))+
rel_wlb +labs(title = paste("Wheat leaf blast"))+
rel_xy +labs(title = expression(bolditalic("N. tabacum-X. fastidiosa")))+
rel_plb +labs(title = paste("Potato late blight"))+guide_area()+
plot_layout(ncol =3, guides = "collect")+
plot_annotation(tag_levels = 'A')&
theme_minimal_grid(font_size = 9)&
theme(plot.title = element_text(size =8))&
xlim(0,100)
ggsave("figs/relative_influence_combo.png", dpi = 300, height = 5, width = 6)
conc_sbr+labs(title = paste("Soybean rust"),
subtitle = paste("RMSE = ",round(accuracy_sbr$RMSE,2),", ",
"CCC = ",round(accuracy_sbr$CCC,3)))+
conc_calo+labs(title = paste("Calonectria leaf blight"),
subtitle = paste("RMSE = ",round(accuracy_calo$RMSE,2),", ",
"CCC = ",round(accuracy_calo$CCC,3)))+
conc_wlb+labs(title = paste("Wheat leaf blast"),
subtitle = paste("RMSE = ",round(accuracy_wlb$RMSE,2),", ",
"CCC = ",round(accuracy_wlb$CCC,3)))+
conc_xy+labs(title = expression(bolditalic("N. tabacum-X. fastidiosa")),
subtitle = paste("RMSE = ",round(accuracy_xy$RMSE,2),", ",
"CCC = ",round(accuracy_xy$CCC,3)))+
conc_pi+labs(title = paste("Potato late blight"),
subtitle = paste("RMSE = ",round(accuracy_pi$RMSE,2),", ",
"CCC = ",round(accuracy_pi$CCC,3)))+
plot_layout(ncol = 3,
widths = c(1, 1,1),
heights = c(1,1))+
plot_annotation(tag_levels = 'A')&
theme_minimal_grid(font_size = 10)&
theme(plot.title = element_text(size =10, face ="bold"),
plot.subtitle = element_text(size =10, face = "plain"))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
ggsave("figs/concordance.png", dpi = 600, height = 6, width = 8)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
`
density_rgb = function(file_dir, limiar, index_cut, cropAbove=T,
left = 0.5, bottom = 0.5, right=1.1, top=1.1){
SBR_low_EX.L1<-stack(paste(file_dir))
SBR_low_EX.L1<-aggregate(SBR_low_EX.L1, fact=5)
SBR_low_EX.L2<-fieldMask(mosaic=SBR_low_EX.L1, myIndex = c(index_cut), cropValue=limiar, cropAbove=cropAbove, plot = F)
cut = mask(SBR_low_EX.L1, SBR_low_EX.L2$newMosaic)
SBR_low_EX.L4<-fieldIndex(mosaic=cut,
# index =index,
plot =F)
SBR_low = RStoolbox::ggRGB(SBR_low_EX.L2$newMosaic,r = 1,g = 2,b = 3)+
theme_map()+coord_fixed()+
theme(panel.background = element_rect(color = NA, fill = NA),
)
RLB_low_comb = data.frame(R = as.data.frame(SBR_low_EX.L4$Red),
G = as.data.frame(SBR_low_EX.L4$Blue),
B = as.data.frame(SBR_low_EX.L4$Green)) %>%
na.omit() %>%
pivot_longer(1:3,names_to = "band", values_to = "value") %>%
# ggplot(aes(value, fill = band))+
# geom_density(color = NA, alpha = 0.8)+
ggplot(aes(value))+
stat_slab(aes(fill = band), alpha = 0.6)+
stat_pointinterval(aes(color = band),
position = position_dodge(width = .6, preserve = "single"),
# color = "gray40",
.width = c(0,0.95))+
scale_fill_manual(values = c("blue", "green","red" ))+
scale_color_manual(values = c("blue", "green","red" ))+
scale_x_continuous(breaks = seq(0,250,50), limits = c(0,300))+
theme_minimal()+
theme(axis.text.y = element_blank(),
panel.grid = element_blank(),
axis.ticks.x = element_line(color = "gray"),
axis.line.x = element_line(color = "gray"))+
labs(x = "Pixel intensity",
y = "",
fill ="")+
guides(color =F)+
inset_element(SBR_low, left, bottom, right, top, align_to = 'full')
RLB_low_comb$patches$layout$widths <- 1
RLB_low_comb$patches$layout$heights <- 1
RLB_low_comb
}
SBR_low = density_rgb("./pics/01-soybean-rust-bg-blue/Ferrugem 52_Median.jpg",
limiar = 100, index_cut = "Blue",
left =0.55,bottom = 0.55,right = 1.1,top = 1.1)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
SBR_high = density_rgb("./pics/01-soybean-rust-bg-blue/Ferrugem 49_Median.jpg",
limiar = 100, index_cut = "Blue",
left =0.55,bottom = 0.55,right = 1.1,top = 1.1)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
SBR_low+SBR_high
calo_low = density_rgb("./pics/01-Calonectria_leaf_bligth/108.jpg",
limiar = 175, index_cut = "Blue",
left =0.5,bottom = 0.5,right = 1.2,top = 1.2)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
calo_high = density_rgb("./pics/01-Calonectria_leaf_bligth/153.jpg",
limiar = 175, index_cut = "Blue",
left =0.5,bottom = 0.5,right = 1.2,top = 1.2)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
calo_low+calo_high
## XY
xy_low = density_rgb("./pics/01-Xylella-tobacco-bg-white/68.jpg",
limiar = 200, index_cut = "Blue",
left =0.5,bottom = 0.5,right = 1.2,top = 1.2)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
xy_high = density_rgb("./pics/01-Xylella-tobacco-bg-white/82.jpg",
limiar = 200, index_cut = "Blue",
left =0.5,bottom = 0.5,right = 1.2,top = 1.2)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
xy_low+xy_high
wlb_low = density_rgb("./pics/01-Wheat_leaf_blast/G_108_R3.jpg",
limiar = 180, index_cut = "Blue")
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
wlb_high = density_rgb("./pics/01-Wheat_leaf_blast/T_239_R1.jpg",
limiar = 180, index_cut = "Blue")
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
wlb_low+wlb_high
plb_low = density_rgb("./pics/01-potato_late_bligh/PI24_6B.png",
limiar = 1, index_cut = "Red", cropAbove = F,
0.55,0.55,1.2,1.1)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
plb_high = density_rgb("./pics/01-potato_late_bligh/PI07_2A.png",
limiar = 1, index_cut = "Red", cropAbove = F,
0.55,0.55,1.2,1.1)
## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
plb_low+plb_high
## COMBO RGB
tags = c("A", "", "B", "", "C", "","D","","E","","F","","G","","H","","I","","J","")
SBR_low+SBR_high+
calo_low+calo_high+
xy_low+xy_high+
wlb_low+wlb_high+
plb_low+plb_high+
plot_layout(widths = c(1, 1),
heights = c(1,1,1,1,1),
tag_level = "keep",
guides = 'collect')+
plot_annotation(tag_levels = list(tags))&
theme(legend.key.size = unit(5, 'mm'),
legend.position = "bottom",
legend.text = element_text(size =10))
ggsave("figs/RGB_dens.png",dpi = 600, height = 8, width =7)
density_rgb2 = function(
file_low = "./pics/01-soybean-rust-bg-blue/Ferrugem 52_Median.jpg",
file_high = "./pics/01-soybean-rust-bg-blue/Ferrugem 49_Median.jpg",
limiar = 100,
index_cut = "Blue",
cropAbove=T,
title = "Soybean Rust",face = "plain"){
#-------------------------------------------------------------------------------------------------
SBR_low_EX.L1<-stack(paste(file_low ))
SBR_low_EX.L1<-aggregate(SBR_low_EX.L1, fact=5)
SBR_low_EX.L2<-fieldMask(mosaic=SBR_low_EX.L1, myIndex = c(index_cut), cropValue=limiar, cropAbove=cropAbove, plot = F)
cut = mask(SBR_low_EX.L1, SBR_low_EX.L2$newMosaic)
SBR_low_EX.L4<-fieldIndex(mosaic=cut,
# index =index,
plot =F)
SBR_low = RStoolbox::ggRGB(SBR_low_EX.L2$newMosaic,r = 1,g = 2,b = 3)+
theme_map()+coord_fixed()+
theme(panel.background = element_rect(color = NA, fill = NA),
plot.title = element_text(size=8, face = "plain"))+
labs(title = "Low")
SBR_low_comb = data.frame(R = as.data.frame(SBR_low_EX.L4$Red),
G = as.data.frame(SBR_low_EX.L4$Blue),
B = as.data.frame(SBR_low_EX.L4$Green)) %>%
na.omit() %>%
pivot_longer(1:3,names_to = "band", values_to = "value") %>%
mutate(sev="Low")
#-------------------------------------------------------------------------------------------------
SBR_high_EX.L1<-stack(paste(file_high))
SBR_high_EX.L1<-aggregate(SBR_high_EX.L1, fact=5)
SBR_high_EX.L2<-fieldMask(mosaic=SBR_high_EX.L1, myIndex = c(index_cut), cropValue=limiar, cropAbove=cropAbove, plot = F)
cut = mask(SBR_high_EX.L1, SBR_high_EX.L2$newMosaic)
SBR_high_EX.L4<-fieldIndex(mosaic=cut,
# index =index,
plot =F)
SBR_high = RStoolbox::ggRGB(SBR_high_EX.L2$newMosaic,r = 1,g = 2,b = 3)+
theme_map()+coord_fixed()+
theme(panel.background = element_rect(color = NA, fill = NA),
plot.title = element_text(size=8, face = "plain"))+
labs(title = "High")
SBR_high_comb = data.frame(R = as.data.frame(SBR_high_EX.L4$Red),
G = as.data.frame(SBR_high_EX.L4$Blue),
B = as.data.frame(SBR_high_EX.L4$Green)) %>%
na.omit() %>%
pivot_longer(1:3,names_to = "band", values_to = "value") %>%
mutate(sev="High")
#-------------------------------------------------------------------------------------------------
all_comb = bind_rows(SBR_low_comb,SBR_high_comb) %>%
mutate(sev =factor(sev, levels =c("Low","High")))
#-------------------------------------------------------------------------------------------------
all_comb %>%
ggplot(aes(value,sev))+
stat_slab(aes(fill = band), alpha = 0.7)+
stat_pointinterval(aes(color = band),
position = position_dodge(width = .5, preserve = "single"),
# color = "gray40",
.width = c(0,0.95))+
scale_fill_manual(values = c("blue", "green","red" ))+
scale_color_manual(values = c("blue", "green","red" ))+
theme_minimal()+
xlim(0,255)+
theme(panel.grid = element_blank(),
plot.title = element_text(size = 10, face = face),
axis.ticks = element_line(color = "gray"),
axis.line = element_line(color = "gray"))+
labs(x = "Pixel intensity",
y = "Severity",
fill ="",
title = paste(title))+
guides(color =F)#+
#SBR_low+SBR_high+
#plot_layout(widths = c(1, .2,.2))
}
SBR_dist = density_rgb2()
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
calo_dist =density_rgb2(file_low ="./pics/01-Calonectria_leaf_bligth/108.jpg",
file_high = "./pics/01-Calonectria_leaf_bligth/153.jpg",
limiar = 175,index_cut = "Blue",cropAbove=T,
title = "Calonectria leaf blight")
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
xy_dist = density_rgb2(file_low ="./pics/01-Xylella-tobacco-bg-white/68.jpg",
file_high = "./pics/01-Xylella-tobacco-bg-white/82.jpg",
limiar = 200,index_cut = "Blue", cropAbove=T,
title = "N. tabacum-X. fastidiosa", face = "italic")
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
wlb_dist = density_rgb2(file_low ="./pics/01-Wheat_leaf_blast/G_108_R3.jpg",
file_high = "./pics/01-Wheat_leaf_blast/T_239_R1.jpg",
limiar = 180,index_cut = "Blue",cropAbove=T,
title = "Wheat leaf blast")
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## [1] "3 layers available"
## [1] "Mask equation myIndex=Blue"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
plb_dist = density_rgb2(file_low ="./pics/01-potato_late_bligh/PI24_6B.png",
file_high = "./pics/01-potato_late_bligh/PI07_2A.png",
limiar = 1,index_cut = "Red",cropAbove=F,
title = "Potato late blight")
## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## [1] "3 layers available"
## [1] "Mask equation myIndex=Red"
## [1] "3 layers available"
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
(SBR_dist+calo_dist+xy_dist+wlb_dist+plb_dist+guide_area())+
plot_layout(nrow = 3,
ncol = 2,
tag_level = "keep",
# widths = c(1,1,1,1,1),
guides = 'collect')+
plot_annotation(tag_levels = "A")+
# plot_annotation(tag_levels = list(tags2))&
theme(legend.key.size = unit(5, 'mm'),
legend.position = "none",
legend.text = element_text(size =10))
ggsave("figs/RGB_dens2.png",dpi = 600, height = 7, width =6)